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Code review, refactoring, debugging, maintainability, and engineering quality
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- Hyperbolic Learning Brain Graphs HlbgHyperbolic Learning on Brain Graphs (HLBG) methodology for brain disorder diagnosis using Lorentzian hyperbolic space and Graph-aware Mamba (GaMamba). Models hierarchical relationships among ROIs, functional communities, and whole-brain networks via geometric entailment constraints. Activation: hyperbolic brain graphs, brain network diagnosis, GaMamba, hyperbolic space brain, Lorentz model brain, hierarchical brain representation.Votes: 0GitHub stars: 3
- Generalization Bounds For Deep Learning**arXiv ID:** 2012.04115 **Authors:** Guillermo Valle-Pérez, Ard A. Louis **Published:** 2020-12-07T23:45:09Z **Abstract:** Generalization in deep learning has been the topic of much recent theoretical and empirical research. Here we introduce desiderata for techniques that predict generalization errors for deep learning models in supervised learning. Such predictions should 1) scale correctly with data complexity; 2) scale correctly with training set size; 3) capture differences between arch...Votes: 0GitHub stars: 3
- Continual Lifelong Learning In Natural Language Processing A Survey**arXiv ID:** 2012.09823 **Authors:** Magdalena Biesialska, Katarzyna Biesialska, Marta R. Costa-jussà **Published:** 2020-12-17T18:44:36Z **Abstract:** Continual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is...Votes: 0GitHub stars: 3
- Collaborative Interactive Evolution Of Art In The Latent Space Of Deep Generative Models**arXiv ID:** 2403.19620 **Authors:** Ole Hall, Anil Yaman **Published:** 2024-03-28T17:40:15Z **Abstract:** Generative Adversarial Networks (GANs) have shown great success in generating high quality images and are thus used as one of the main approaches to generate art images. However, usually the image generation process involves sampling from the latent space of the learned art representations, allowing little control over the output. In this work, we first employ GANs that are trained to ...Votes: 0GitHub stars: 3
- Cognitive Deep Machine Can Train Itself**arXiv ID:** 1612.00745 **Authors:** András Lőrincz, Máté Csákvári, Áron Fóthi, Zoltán Ádám Milacski, András Sárkány, Zoltán Tősér **Published:** 2016-12-02T16:49:07Z **Abstract:** Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the obse...Votes: 0GitHub stars: 3
- Arxiv 2404 07968v1Skill generated from arXiv paper 2404.07968v1: AD-NEv++ : The multi-architecture neuroevolution-b...Votes: 0GitHub stars: 3
- Arxiv 2309 13908v1Skill generated from arXiv paper 2309.13908v1: A comparison of controller architectures and learn...Votes: 0GitHub stars: 3
- Arxiv 2201 06321v2Skill generated from arXiv paper 2201.06321v2: Landscape of Neural Architecture Search across sen...Votes: 0GitHub stars: 3
- Arxiv 1807 04587v2Skill generated from arXiv paper 1807.04587v2: Assessing the Scalability of Biologically-Motivate...Votes: 0GitHub stars: 3
- Active Learning Of Inverse Models With Intrinsically Motivated Goal Exploration In Robots**arXiv ID:** 1301.4862 **Authors:** Adrien Baranes, Pierre-Yves Oudeyer **Published:** 2013-01-21T13:26:07Z **Abstract:** We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsi- cally motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve ...Votes: 0GitHub stars: 3
- Non Markovian Kerr Feedback QrcProves unbounded computational superiority of Kerr nonlinear feedback over Gaussian linear reservoirs in continuous-variable quantum reservoir computing. Single Kerr mode with feedback depth D replaces up to ~100 linear modes. Use when: CV-QRC design, non-Markovian reservoir computing, Kerr nonlinear optics, cross-time nonlinear correlations, quantum reservoir capacity analysis, Gaussian limitations, time-delay feedback.Votes: 0GitHub stars: 3
- Tokenize Once Recommend Anywhere Unified Item Tokenization For Multidomain Llmbased Recommendation**arXiv ID:** 2511.12922 **Authors:** Yu Hou, Won-Yong Shin **Published:** 2025-11-17T03:18:04Z **Abstract:** Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item tokenization. However, existing item tokenization methods typically require training separate models for each item domain, limiting generalization. Moreover, the diverse distributions and semantics across ite...Votes: 0GitHub stars: 3
- Simple Tokenizer Agnostic On Policy DistillationSimpleOPD for cross-tokenizer on-policy distillation.Votes: 0GitHub stars: 3
- Serf Towards Better Training Of Deep Neural Networks Using Logsoftplus Error Activation Function**arXiv ID:** 2108.09598 **Authors:** Sayan Nag, Mayukh Bhattacharyya **Published:** 2021-08-21T23:33:57Z **Abstract:** Activation functions play a pivotal role in determining the training dynamics and neural network performance. The widely adopted activation function ReLU despite being simple and effective has few disadvantages including the Dying ReLU problem. In order to tackle such problems, we propose a novel activation function called Serf which is self-regularized and nonmonotonic in n...Votes: 0GitHub stars: 3
- Qiskit Llm Code MigrationLLM+RAG methodology for automated Qiskit code migration across versions. Uses taxonomy-based RAG to reduce hallucinations and improve code reliability in Quantum Software Engineering (QSE). Activation: qiskit migration, quantum code migration, QDK version upgrade, quantum API refactoring, quantum software engineering, quantum development kit, quantum code maintenance, LLM quantum code, RAG quantum migration, Qiskit version, quantum technical debtVotes: 0GitHub stars: 3
- Physics Transformer Pde Function ProjectionPhysics Transformer methodology for PDE prediction using function-projection-based tokenization. Treats physical fields as continuous functions with adaptive local basis functions and locality-preserving spatial patches.Votes: 0GitHub stars: 3
- Openai O1 System Cardo1 chain-of-thought reasoning and deliberative alignment methodology - RL-based reasoning training for improved safetyVotes: 0GitHub stars: 3
- Openai Gpt 5 System CardGPT-5 system architecture and safety methodology - unified model with reasoning router, safe-completions, and deliberative alignmentVotes: 0GitHub stars: 3
- Neurocogmap Llm Cognitive OrganizationNeuroCogMap framework for mapping cognitive functions in LLMs using neuroscience-inspired methodology. Analyzes hallucination, bias, refusal, sycophancy, and memory capabilities via parcel-functional annotation and cross-model functional correspondence.Votes: 0GitHub stars: 3
- Neural Simulator Openai Gym BridgeNeural Simulator OpenAI Gym BridgeVotes: 0GitHub stars: 3
- Llm Decision Centric DesignDecision-Centric framework for LLM systems that separates decision signals from action policies. Apply this when designing LLM control flow, routing, adaptive inference, or building diagnosable agent systems.Votes: 0GitHub stars: 3
- Llm Code Generation SurveySkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Cross Tokenizer On Policy DistillationCross-Tokenizer On-Policy Distillation framework using byte-prefix marginalization to enable knowledge transfer between models with different tokenizers while preserving policy quality.Votes: 0GitHub stars: 3
- Cheesebench Evaluating Large Language ModelsWe introduce CheeseBench, a benchmark that evaluates large language models (LLMs) on nine classical behavioral neuroscience paradigms (Morris water maze, Barnes maze, T-maze, radial arm maze, star maz. Activation: rodent behavior paradigms, LLM evaluation, ODE complexityVotes: 0GitHub stars: 3