Surrogate Neural Architecture Codesign Package (SNAC-Pack)
SNAC-Pack open-source AutoML framework for hardware-aware NAS targeting FPGA deployment with multi-dimensional budget constraints.
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SNAC-Pack open-source AutoML framework for hardware-aware NAS targeting FPGA deployment with multi-dimensional budget constraints.
Clockless FPGA-based neuromorphic architecture uses spiking dynamics for machine learning with spike-encoded audio classification.
Hybrid architecture combining LLMs and GNNs for relational database foundation models that preserve relational context.
Numerical study of neural network surrogate model performance for uncertainty propagation in stochastic boundary value problems.
EU-based Anthropic users report account suspensions with unresponsive support, raising questions about Claude service reliability and appeal processes.
EU GPU price tracking over 50 days shows RTX 5090 rising 3% while mid-range cards drop 7-9%, relevant for local inference hardware planning.
Neuro-symbolic approach couples large reasoning models with model checkers for reactive hardware synthesis via iterative Verilog repair.
Croissant Baker enables automated Croissant metadata generation for governed ML datasets without public platform upload, addressing NeurIPS dataset track requirements.
NeuroTrain survey establishes taxonomy of spiking neural network training algorithms with open benchmarking framework spanning surrogate-gradient and biologically-inspired methods.
There's alot more than what i mentioned, and alot more to add, but figured i'd share as it's currently a work in progress and could help educate people and make it an easy source to get this type of information... Link: [Culture Wars — the data behind America's…
Non-monotone preconditioned trust-region method (NAPTS) for parallel neural network training using domain decomposition.
Comparison of LSTM, GNN, and Transformer architectures for NBA player movement forecasting in signal processing.
Formalizes interestingness via Kolmogorov Complexity to predict which tasks drive future compression progress in self-improving systems.
Information Filtering Networks and Homological Neural Networks combined to study compositional sparsity as structural prior for DNN design.
RNN-ProVe probabilistically verifies RNN-based policies in partially observable RL without restrictive assumptions or coarse approximations.
NeuroAtlas: largest EEG benchmark (42 datasets, 260k hours) evaluating foundation model generalization across clinical neurophysiology tasks.
Hodge decomposition applied to neural operators for physics simulation, using topological structure to isolate unlearnable and learnable components.
VERIME combines LLMs with SMT solvers to audit natural-language specs, detecting ambiguity and safety violations in safety-critical requirements.
Parallel-scan recurrent neural networks enable scalable variational Monte Carlo for quantum many-body systems via parallelizable RNN architectures.
Di-BiLPS neural framework solves PDEs under extremely sparse observations via denoising-induced bidirectional latent solvers with efficient inference.
Active learning framework for machine-learning interatomic potentials scales to 200k structures via neural tangent kernels.
Neural ODE surrogate model for metropolitan flood forecasting using conditional latent dynamics network.
Expressivity hierarchy for hypergraph neural networks via homomorphism density completeness.
Anthropic launches Claude For Legal with practice-area plugins and MCP connectors to DocuSign, LexisNexis, Thomson Reuters, and six other legal platforms.
Hello everyone, Presenting at a top-tier conference for the first time and having a very hard time coming up with an appropriate design for my poster. Everything I do seems basic and banal. My paper is more theory-oriented, and apart from putting math formulas in bold in…
CP-SynC-XL benchmark (100 problems, 4,577 instances) evaluates declarative vs. algorithmic paradigms for LLM-synthesized combinatorial solvers.
Random Matrix Theory detects overfitting onset in neural networks via Correlation Traps without accessing train/test data.
Semi-supervised confidence detection in speech using Whisper encoder with uncertainty-aware pseudo-labeling.
MetaColloc meta-learns neural basis functions offline to solve PDEs at test time without retraining, replacing optimization with collocation assembly.