2026-03-20 04:41:08
Most developers now use coding assistants. I do too—Copilot at work, Claude Code at home. As a developer, I prefer not to repeat myself. This post explains why and how to avoid repetition as a skill.
The DRY principle has been present in the software development field for ages. The idea is that if you copy and paste code in multiple places and a bug appears, you'll need to fix the bug in all these places. The more the number of duplications, the more chances you'll miss one of them when fixing.
\ In the context of coding assistants, DRY means something entirely different. From a personal point of view, it means you don't need to ask the assistant to analyze your project at every session. Or to reference your language's coding conventions again. Or that your project favors immutability.
\ When a new developer joins a good project, they are told what the project's features, architecture, and conventions are. In great projects, conventions are formalized in written form and sometimes even kept up-to-date. Your coding assistant is a team member like any other: it requires written documentation.
Both coding assistants I use allow the use of such instructions. I assume all do. For example, GitHub Copilot automatically reads the .github/instructions.md file.
\ The problem with such instructions is that the assistant may read them automatically. If they are too broad and don't apply to the tasks, you'll pollute the context with irrelevant data. Conclusion: You need to keep the file relatively small. Thus, focus file instructions on general information.
\ Sizing advices differ depending on the exact source. For example, docs.factory.ai mentions less than 150 lines. A Q&A on Reddit clarifies that 700 lines is too long. My advice would be to start small, increment when needed, analyze results, and refactor when it grows too large.
Today, critical resources aren't CPU, RAM, or storage, but tokens. Tokens are a finite and expensive resource. My opinion is that soon, developers will be measured on their token usage: the better one will be the one using the fewest tokens to achieve similar results.
\
Most agents will load the default instructions file, AGENTS.md. Claude will load CLAUDE.md. The smaller the file, the fewer tokens used in the context.
\ A good context contains all the necessary tokens, but not more. That's where skills come into play.
Agent Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently.
\ — Agent skills
\
Skills are different from AGENTS.md in that they aren't loaded automatically. Hence, they don't bloat the context. Now, comes the fun part: depending on the coding assistant, it may discover the skill and load it automatically, or not.
\ I will describe how Claude works. I think its approach is superior, so I guess other assistants will implement it soon.
By default, both you and Claude can invoke any skill. You can type
/skill-nameto invoke it directly, and Claude can load it automatically when relevant to your conversation.
\
Claude knows when to invoke a skill by transforming all SKILL.mdfiles' front matter into tools. Here's the front matter of the Kotlin skill I created: \n
---
name: kotlin
description: "|"
Use this skill when working with Kotlin code in any capacity:
- Reading, writing, editing, or reviewing Kotlin files (*.kt, *.kts)
- Running Gradle tasks for Kotlin modules
- Writing or debugging Kotlin/JS code that targets Node.js
- Working with external JavaScript libraries from Kotlin
- Writing tests for Kotlin code (@Test, @BeforeTest, @AfterTest)
- Setting up dependency injection or mocking in Kotlin
- Dealing with multiplatform Kotlin projects (common, jsMain, jsTest, jvmMain)
- Troubleshooting Kotlin compilation or runtime errors
- Any task involving Kotlin/JS modules targeting Node.js
This skill provides Kotlin/JS technical knowledge (especially JS interop gotchas)
and coding style preferences beyond the official conventions.
---
The above gives Claude information on when to invoke the tool. Obviously, the skill is about Kotlin and Kotlin/JS.
\
For the content, follow the same rules as regular AGENTS.md files:
SKILL.md summarizes the main items and links to the conventions. The full conventions are cached locally in this skill directory at `kotlin-coding-conventions.md`.
Key reminders:
- Prefer `val` over `var` - immutability first
- Use immutable collection interfaces (`List`, `Set`, `Map`, not `MutableList`, etc.)
- Use `it` for short lambdas, named parameters for nested/complex ones
- Prefer expression form of `if`, `when`, `try` over statement form
- Prefer functional style (`filter`, `map`) over imperative loops
You should use good and bad examples.
\
### 1. External Interfaces vs Kotlin Classes
**Problem**: Kotlin classes have methods on their prototype. External interfaces expect methods directly on the object. You cannot use `unsafeCast` to convert between them.
kotlin // ❌ WRONG: This will fail at runtime class MockSql { fun unsafe(query: String) = … } val sql: Sql = mockSql.unsafeCast() // Will throw: sql.unsafe is not a function // ✅ CORRECT: Use extension function to build plain JS object fun MockSql.toExternal(): Sql { val obj = Any().asDynamic() val mock = this
obj.unsafe = { query: String -> mock.unsafe(query) }
obj.end = { mock.end() }
return obj.unsafeCast<Sql>()
}
// Then use it:
val sql: Sql = mockSql.toExternal()
\
Skills are great for avoiding repeating the same instructions over and over. The main difference with instructions is that they don't bloat the context for nothing. Depending on your coding assistants, they may trigger when required, or you may need to explicitly activate them.
\ The complete source code for this post can be found on Codeberg.
To go further:
Originally published at A Java Geek on March 15th, 2026.
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2026-03-20 04:38:56
Zug, Switzerland, March 19th, 2026/Chainwire/--Following six months of testing with zero state divergence, Igra Network opens public access to a 3,000+ TPS smart contract environment secured by proof-of-work consensus. Fifteen protocols are deploying at launch alongside cross-chain connectivity through Hyperlane. A security audit by Sigma Prime completed with no unresolved issues.
Igra Labs has opened public access to Igra Network, a decentralized EVM-compatible execution layer built on Kaspa's proof-of-work BlockDAG. The mainnet launch follows a testnet that processed over 730,000 transactions across 21 million blocks with zero state divergence.
Kaspa is a proof-of-work blockchain with a market capitalization nearing $1 billion and more than 500,000 active addresses. The ecosystem generated $486 million in trading volume on the day KRC-20 token protocol functionality launched, demonstrating significant latent demand for on-chain activity.
Despite that demand, the ecosystem has operated with less than $1 million in DeFi total value locked due to the absence of a decentralized and programmable smart contract layer. Igra Network is built to close that gap.
By inheriting Kaspa's proof-of-work security while delivering full Ethereum Virtual Machine compatibility, the network gives the ecosystem's existing user base and a global developer community of over 100,000 Solidity engineers a shared execution environment for the first time.
Igra operates as a based rollup, a design in which transaction ordering is delegated entirely to the base layer rather than handled by a centralized sequencer. Kaspa miners sequence Igra transactions without the ability to read their contents, a structural property that provides resistance to MEV extraction, front-running, and transaction censorship at the protocol level rather than as an application-layer patch.
The network delivers over 3,000 transactions per second with sub-second inclusion latency, powered by Kaspa's 10-block-per-second BlockDAG architecture and parallel transaction sequencing. Unlike linear blockchains where transactions queue in a single ordering chain, the BlockDAG processes multiple blocks simultaneously, providing the throughput required for DeFi workloads at scale. A security audit by Sigma Prime, the firm behind Ethereum's Lighthouse consensus client, completed clean with no unresolved issues.
Fifteen protocols have committed to deploy at launch spanning DeFi, infrastructure, wallets, and stablecoins. Launch partners include Kaskad (Aave V3-style lending and borrowing), ZealousSwap (Uniswap v2 decentralized exchange), Zealous Auctions Protocol (Continuous Clearing Auctions token launch), Hyperlane (cross-chain messaging and USDC.e bridging), Kasperia and Kasware (wallets), KAT Bridge (KRC-20 Token and KRC-721 NFT bridging), Dagscan (block explorer), and Kaspa.com (DEX and launchpad).
Ecosystem partners collectively manage over $5 million in total value locked across the Kaspa ecosystem. Kaspa's native token wraps 1:1 to iKAS on Igra through a trust-minimized bridge backed by locked KAS on L1, serving as the network's gas token.
Igra Labs plans to introduce a second-generation execution engine incorporating Block-STM parallel processing in the second half of 2026, alongside agent-native infrastructure for machine-to-machine payment, identity, and orchestration, positioning the network for the emerging autonomous agent economy.
"There is over a billion dollars in ecosystem value on Kaspa and $486 million in volume on a single day when KRC-20 launched, yet almost no sufficiently decentralized programmable infrastructure exists to capture it," said Pavel Emdin, CEO of Igra Labs.
\
"That gap is now closed. Igra delivers full EVM programmability without compromising on the security properties that brought people to proof-of-work.""Fifteen teams committed before mainnet went live. Hyperlane gives us cross-chain connectivity and stablecoin access from day one, and Kaskad brings institutional-grade lending to proof-of-work for the first time," said Ashton Wood, Head of Ecosystem and Business Development at Igra Labs. "The infrastructure is live and the ecosystem is ready."
The Igra Labs core team includes former DAGLabs engineers who contributed to shipping Kaspa's original mainnet, alongside Panther Protocol alumni and EVM client contributors. The project is governed by a Swiss association, with a functioning DAO governance structure following a successful token generation event.
A public token auction for the IGRA governance and security token is scheduled for late March 2026 through ZAP (Zealous Auctions Protocol), an on-chain continuous clearing auction on Igra Network (https://igralabs.com/public-auction/overview).
The same mechanism powered Aztec's $59 million sale—on-chain price discovery, no lockup or vesting, tokens fully liquid on claim. Participation is open to anyone with iKAS on the network ($0.006 floor; three-point-five percent of supply). Details at igralabs.com. Secondary on-chain trading through ZealousSwap DEX.
Igra Network is a based rollup on Kaspa's proof-of-work BlockDAG delivering full EVM compatibility, 3,000+ TPS, sub-second finality, and architectural MEV resistance without a centralized sequencer. Learn more at igralabs.com (https://igralabs.com).
Contact
CEO
Pavel Emdin
Igra Association
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program
:::
Disclaimer:
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
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2026-03-20 04:15:20
:::tip GAMEE generated an Estimated $3.54 million total revenue in 2025, representing a three-year CAGR of 112%. The transaction values GAMEE at an $18 million valuation, incorporating EBITDA-contingent earn-outs designed to mitigate upfront consideration risk over a two-year performance period.
:::
\ Tortola, British Virgin Islands — March 19, 2026
AlphaTON Capital Corp. (Nasdaq: ATON), a public technology company dedicated to scaling the Telegram super-app ecosystem, today announced it has entered into a definitive agreement to acquire a 60% controlling interest in GAMEE, a leading mobile gaming platform and wholly owned subsidiary of Animoca Brands. Concurrently, AlphaTON and Animoca Brands have formalized a Strategic Alliance to pursue broader commercial opportunities across blockchain and social gaming.
GAMEE is one of the most established gaming platforms operating at the intersection of Web2 and Web3, boasting over 119 million registered users and a history of more than 10 billion gameplay sessions. It holds a particularly strong foothold within the Telegram ecosystem, where it serves over 61 million users. The acquisition will provide AlphaTON with immediate, scaled user engagement across Telegram's approximately one billion addressable users, a distribution advantage management believes is unmatched among publicly traded digital asset companies.
Concurrent with entering into the acquisition purchase agreement, AlphaTON and Animoca Brands, which is one of the most prominent investors in Web3 infrastructure globally, formalized a Strategic Alliance to identify and develop commercial opportunities across blockchain and social gaming. The alliance combines AlphaTON's position as the leading public company scaling the Telegram ecosystem with Animoca Brands' unparalleled Web3 portfolio and institutional relationships. Both organizations share a commitment to advancing digital property rights and broadening Web3 accessibility through the Telegram platform.
Under the terms of the agreement, AlphaTON Capital will acquire a 60% controlling interest stake and assume day-to-day management of GAMEE for a total consideration of up to $11 million structured to include a performance-linked earn-out over two years. Animoca Brands has signed a two-year standstill agreement with AlphaTON Captial that prohibits Animoca Brands from acquiring a controlling position in AlphaTON Capital.
I. Consideration at Closing: $3.5 million
II. Year 1 Earn-Out: Up to $3.5 million
III. Year 2 Earn-Out: Up to $4.0 million
The transaction includes a transfer of digital assets from Animoca Brands' treasury:
The signing of the SPA today reflects the shared vision of Animoca Brands and AlphaTON Capital to promote digital property rights and expand Web3 accessibility on a large scale by leveraging Telegram. The transaction is expected to close within 30 days, subject to customary closing conditions.
The entire team at GAMEE are expected to stay on board and continue to operate the GAMEE. Martin Zakovec will continue as CEO, Miroslav Chmelka as Co-Founder and CTO, and Founder Bozena Rezab will move into a strategic role as Head of AlphaTON Gaming at closing. The board of GAMEE will consist of three members, two from AlphaTON and one from Animoca Brands.
This acquisition is a transformative milestone for AlphaTON Capital," said Brittany Kaiser, CEO of AlphaTON Capital. "By bringing GAMEE’s 119 million users and deep Telegram integration under our umbrella, we are not just acquiring a gaming platform, we are capturing a massive, active gateway to the next billion Web3 users. Our alliance with Animoca Brands further solidifies our position as the primary engine scaling the Telegram super-app ecosystem. We are ready to leverage our operational expertise to drive GAMEE’s EBITDA growth and deliver long-term value to our shareholders.
Bozena Rezab, founder of GAMEE and, on closing, Head of AlphaTON Gaming, commented: "Our mission has always been to onboard the masses to Web3 through the power of play. By deepening our synergy with AlphaTON Capital, we are not just providing entertainment; we are giving our 119 million players true digital ownership and a gateway into a broader, interconnected ecosystem. This next phase is about turning players into stakeholders.”
Yat Siu, Co-founder and Executive Chairman of Animoca Brands, said: "GAMEE has consistently proven that gaming is the 'killer app' for blockchain adoption. We are thrilled to support GAMEE and AlphaTON as they continue to build gaming on Telegram, IOS and Android. This collaboration reinforces our commitment to building a digital economy where every user has the opportunity to own their digital assets and benefit from their contributions."
AlphaTON Capital Corp (NASDAQ: ATON) is a publicly-traded technology company scaling the Telegram super app, with an addressable market of 1 billion monthly active users. The Company is delivering a comprehensive hyperscaler strategy on the Telegram ecosystem through a combination of software products, middleware assets, and AI infrastructure hardware clusters deploying Confidential AI for the Telegram ecosystem.
Through its operations, AlphaTON Capital provides public market investors with institutional-grade exposure to the Telegram ecosystem and its 1 billion-user platform while maintaining the governance standards and reporting transparency of a Nasdaq-listed company. The Company's activities span AI Confidential Compute, network validation and staking operations, development of Telegram-based applications including strategic investments and acquisitions of decentralized finance platforms, gaming and markets, and business applications.
AlphaTON Capital Corp is incorporated in the British Virgin Islands and trades on Nasdaq under the ticker symbol "ATON". AlphaTON Capital, through its legacy business, is also advancing first-in-class therapies targeting known checkpoint resistance pathways to achieve durable treatment responses and improve patients' quality of life. AlphaTON Capital actively engages in the drug development process and provides strategic counsel to guide the development of novel immunotherapy assets and asset combinations.
Website: https://alphatoncapital.com
Telegram: https://t.me/alphatoncapital_official
X: https://x.com/AlphaTONCapital
LinkedIn: https://www.linkedin.com/company/alphaton-capital/
Stocktwits: https://stocktwits.com/AlphaTONCapital_Official
GAMEE was founded in 2015 and has been a subsidiary of Animoca Brands since 2020. GAMEE is a high-engagement mobile gaming platform focused on onboarding a mass gaming audience to Web3. It has over 119 million registered users and has served over 10 billion gameplay sessions across multiple ecosystems. GAMEE’s WATCoin airdrop collectively onboarded 4 million user wallets into the TON ecosystem. The company has partnered with over 40 major Web3 communities including Mocaverse, TON, Notcoin, The Sandbox, and Cool Cats. Learn more at www.gamee.com or get updates by following on X.
Animoca Brands Corporation Limited (ACN: 122 921 813) is a global digital assets leader building and investing in impactful technologies and ecosystems to reimagine future economies. It has received broad industry and market recognition including Fortune Crypto 40, Top 50 Blockchain Game Companies 2025, Financial Times’ High Growth Companies Asia-Pacific, and Deloitte Tech Fast. Animoca Brands is recognized for building digital asset platforms such as the Moca Network, Open Campus, Anichess, and The Sandbox, as well as institutional-grade platforms; providing digital asset services to help Web3 companies launch and grow; and investing in frontier Web3 technology, with a portfolio of over 600 companies and digital assets. For more information visit www.animocabrands.com or follow on X, YouTube, Instagram, LinkedIn, Facebook, and TikTok.
All statements in this press release, other than statements of historical facts, including without limitation, statements regarding the Company’s business strategy, plans and objectives of management for future operations and those statements preceded by, followed by or that otherwise include the words “believe,” “expects,” “anticipates,” “intends,” “estimates,” “will,” “may,” “plans,” “potential,” “continues,” or similar expressions or variations on such expressions are forward-looking statements. Forward-looking statements in this press release include statements concerning, among other things, the Company’s expectations that the GAMEE acquisition will provide the Company with immediate, scaled user engagement across Telegram's users; the Company’s belief that its distribution advantage will be unmatched among publicly traded digital asset companies; the Company’s plans for the Strategic Alliance to support the growth of GMEE, TON and WAT; the Company’s plans to pursue and develop broader commercial opportunities across blockchain and social gaming; the Company’s commitment to advance digital property rights and broaden Web3 accessibility through the Telegram platform; the Company’s plans to acquire a controlling interest stake and assume day-to-day management of GAMEE; the timing of the transaction closing; the Company’s expectation to capture an active gateway to the next billion Web3 users; the plans to turn players into stakeholders; the Company’s plans, through its legacy business, to advance first-in-class therapies to achieve durable treatment responses and improve patients' quality of life; the Company’s plans to drive GAMEE EBITDA growth and deliver long-term value to its shareholders; and other statements that are not historical fact. As a result, forward-looking statements are subject to certain risks and uncertainties, including, but not limited to: the risk that the proposed transaction may not be completed in a timely manner or at all, the possibility that various closing conditions for the transaction may not be satisfied or waived, the occurrence of any event, change or other circumstance that could give rise to the termination of the agreement, the potential that the strategic benefits, synergies or opportunities expected from the proposed transaction may not be realized or may take longer to realize than expected, the timing, progress and results of the Company’s strategic initiatives, the Company’s reliance on third parties, the operational strategy of the Company, the Company’s executive management team, risks from Telegram’s platform and ecosystem, the potential impact of markets and other general economic conditions, and other factors set forth in “Item 3 – Key Information – Risk Factors” in the Company’s Annual Report on Form 20-F for the year ended March 31, 2025 and included in the Company’s Form 6-Ks filed with the Securities and Exchange Commission on September 3, 2025 and January 13, 2026. Although the Company believes that the expectations reflected in these forward-looking statements are reasonable, undue reliance should not be placed on them as actual results may differ materially from these forward-looking statements. The forward-looking statements contained in this press release are made as of the date hereof, and the Company undertakes no obligation to update publicly or revise any forward-looking statements or information, except as required by law.
AlphaTON Capital Investor Relations
[email protected] \n +1 (203) 682-8200
AlphaTON Capital Media Inquiries
Richard Laermer, RLM PR \n [email protected] \n +1 (212) 741-5106 X 216
Animoca Brands
\
:::tip This story was distributed as a release by Blockchain Wire under HackerNoon Business Blogging Program.
:::
\
2026-03-20 04:00:46
4 Measuring Catastrophic Forgetting
\
In this work, we sought to improve our understanding of catastrophic forgetting in ANNs by revisiting the fundamental questions of (1) how we can quantify catastrophic forgetting, and (2) how do the choices we make when designing learning systems affect the amount of catastrophic forgetting that occurs during training. To answer these questions we explored four metrics for measuring catastrophic forgetting: retention, relearning, activation overlap, and pairwise interference. We applied these four metrics to four testbeds from the reinforcement learning and supervised learning literature and showed that (1) catastrophic forgetting is not a phenomenon which can be effectively described by either a single metric or a single family of metrics, and (2) the choice of which modern gradient-based optimizer is used to train an ANN has a serious effect on the amount of catastrophic forgetting. Our results suggest that users should be wary of the optimization algorithm they use with their ANN in problems susceptible to catastrophic forgetting—especially when using Adam but less so when using SGD. When in doubt, we recommend simply using SGD without any kind of momentum and would advise against using Adam. Our results also suggest that, when studying catastrophic forgetting, it is important to consider many different metrics. We recommend using at least a retention-based metric and a relearning-based metric. If the testbed prohibits using those metrics, we recommend using pairwise interference. Regardless of the metric used, though, research into catastrophic forgetting—like much research in AI—must be cognisant that different testbeds are likely to favor different algorithms, and results on single testbeds are at high risk of not generalizing.
While we used various testbeds and metrics to quantify catastrophic forgetting, we only applied it to answer whether one particular set of mechanisms affected catastrophic forgetting. Moreover, no attempt was made to use the testbed to examine the effect of mechanisms specifically designed to mitigate catastrophic forgetting. The decision to not focus on such methods was made as Kemker et al. (2018) already showed that these mechanisms’ effectiveness varies substantially as both the testbed changes and the metric used to quantify catastrophic forgetting changes. Kemker et al., however, only considered the retention metric in their work, so some value exists in looking at these methods again under the broader set of metrics we explore here. In this work, we only considered shallow ANNs. Contemporary deep learning frequently utilizes networks with many—sometimes hundreds—of hidden layers. While, Ghiassian, Rafiee, Lo, et al. (2020) showed that this might not be the most impactful factor in catastrophic forgetting (p. 444), how deeper networks affect the nature of catastrophic forgetting remains largely unexplored. Thus further research into this is required.
\ One final opportunity for future research lies in the fact that, while we explored several testbeds and multiple metrics for quantifying catastrophic forgetting, there are many other, more complicated testbeds, as well as several still-unexplored metrics which also quantify catastrophic forgetting (e.g., Fedus et al. (2020)). Whether the results of this work extend to significantly more complicated testbeds remains an important open question, as is the question of whether or not these results carry over to the control case of the reinforcement learning problem. Notably, though, it remains an open problem how exactly forgetting should be measured in the control case.
The authors would like to thank Patrick Pilarsky and Mark Ring for their comments on an earlier version of this work. The authors would also like to thank Compute Canada for generously providing the computational resources needed to carry out the experiments contained herein. This work was partially funded by the European Research Council Advanced Grant AlgoRNN to Jürgen Schmidhuber (ERC no: 742870).
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2026-03-20 03:47:02
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2026-03-20 03:00:44
4 Measuring Catastrophic Forgetting
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The results provided in Section 6 allow us to reach several conclusions. First and foremost, as we observed a number of differences between the different optimizers over a variety of metrics and in a variety of testbeds, we can safely conclude that there can be no doubt that the choice of which modern gradient-based optimization algorithm is used to train an ANN has a meaningful and large effect on catastrophic forgetting. As we explored the most prominent of these, it is safe to conclude that this effect is likely impacting a large amount of contemporary work in the area.
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We earlier postulated that Adam being viewable as a combination of SGD with Momentum and RMSProp could mean that if either of the two mechanisms exacerbated catastrophic forgetting, then this would carry over to Adam. Thus, it makes sense to look at how often each of the four optimizers was either the best or second-best under a given metric and testbed. This strategy for interpreting the above results is supported by the fact that—in many of our experiments–the four optimizers could be divided naturally into one pair that did well and one pair that did poorly. The results of this process are shown in Figure 6. Looking at Figure 6, it is very obvious that Adam was particularly vulnerable to catastrophic forgetting and that SGD outperformed the other optimizers overall. These results suggest that Adam should generally be avoided—and ideally replaced by SGD—when dealing with a problem where catastrophic forgetting is likely to occur. We provide the exact rankings of the algorithms under each metric and testbed in Appendix D. When looking at SGD with Momentum as a function of momentum and RMSProp as a function of the coefficient of the moving average, we saw evidence that these hyperparameters have a pronounced effect on the amount of catastrophic forgetting.
\ Since the differences observed between vanilla SGD and SGD with Momentum can be attributed to the mechanism controlled by the momentum hyperparameter, and since the differences between vanilla SGD and RMSProp can be similarly attributed to the mechanism controlled by the moving average coefficient hyperparameter, this is in no way surprising. However, as with what we observed with α, the relationship between the hyperparameters and the amount of catastrophic forgetting was generally smooth; similar values of the hyperparameter produced similar amounts of catastrophic forgetting. Furthermore, the optimizer seemed to play a more substantial effect here. For example, the best retention and relearning scores for SGD with Momentum we observed were still only roughly as good as the worst such scores for RMSProp.
\ Thus while these hyperparameters have a clear effect on the amount of catastrophic forgetting, it seems unlikely that a large difference in catastrophic forgetting can be easily attributed to a small difference in these hyperparameters. One metric that we explored was activation overlap. While French (1991) argued that more activation overlap is the cause of catastrophic forgetting and so can serve as a viable metric for it (p. 173), in the MNIST testbed, activation overlap seemed to be in opposition to the well-established retention and relearning metrics. These results suggested that, while Adam suffers a lot from catastrophic forgetting, so too does RMSProp. Together, this suggests that catastrophic forgetting cannot be a consequence of activation overlap alone. Further studies must be conducted to understand why the unique representation learned by RMSProp here leads to it performing well on the retention and relearning metrics despite having a greater representational overlap. On the consistency of the results, the variety of rankings we observed in Section 6 validate previous concerns regarding the challenge of measuring catastrophic forgetting. Between testbeds, as well as between different metrics in a single testbed, vastly different rankings were produced.
\ While each testbed and metric was meaningful and thoughtfully selected, little agreement appeared between them. Thus, we can conclude that, as we hypothesized, catastrophic forgetting is a subtle phenomenon that cannot be characterized by only limited metrics or limited problems. When looking at the different metrics, the disagreement between retention and relearning is perhaps the most concerning. Both are derived from principled, crucial metrics for forgetting in psychology. As such, when in a situation where using many metrics is not feasible, we recommend ensuring that at least retention and relearning-based metrics are present. If these metrics are not available due to the nature of the testbed, we recommend using pairwise interference as it tended to agree more closely with retention and relearning than activation overlap. That being said, more work should be conducted to validate these recommendations.
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