Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
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Raw feed is chronological evidence. Signals ranks and interprets material change.
Research proposes PR-GAN and counterfactual methods to generate synthetic bearing vibration data with user-specified fault probabilities.
Research presents memory-optimal inference artifacts for transformer attention using Mathematics of Arrays (MoA), improving KV-cache efficiency.
Research explores methods for estimating total variation distance between autoregressive model distributions, accounting for inference optimizations.
Research introduces a basis-independent measurement of trained triangle-loop products in sheaf neural networks to analyze internal mechanisms like rotation.
GeoDES, a new diffusion-based model, aims to improve storm-centered weather prediction by augmenting limited regional historical data.
Researchers propose SynPre-FL, a framework integrating synthetic data pretraining with federated learning to improve privacy-preserving risk prediction.
Research explores federated learning for privacy-preserving, transparent, and scalable multimodal breast cancer prediction models.
Researchers propose a novel differentially private training framework for deep neural networks that selectively privatizes inputs, not labels.
Research identifies conditions where knowledge graphs enhance reinforcement learning performance, rather than being neutral or detrimental.
New research on the Lattice Deduction Transformer (LDT) reveals neural 'solvers' make one-shot predictions rather than iterative deductions.
Research explores expert-guided editing for time-series foundation model forecasts, allowing human feedback to revise model-generated trajectories.
Research finds optimizing reward model inference speed in RLHF pipelines significantly reduces bottlenecks, suggesting C++ implementations over PyTorch.
Koopman Dreamer proposes a spectrally constrained latent dynamics core for world models to improve stability and control in long imagined trajectories.
Research finds DreamerV3-family model-based RL agents catastrophically forget tasks, with the 'actor' component, not the 'world model', being the source of forgetting.
AlphaRoute, a new adaptive search framework using LLMs, optimizes multi-objective routing in VLSI design, addressing NP-hard combinatorial problems.
Research introduces an isotropy-preserving spectral cap for Muon, a matrix-sign optimizer, to better understand its impact on LLM weight matrices.
OPIUM is a training-free method introduced to mitigate unintended externalities like over-refusal and weakened safety from LLM activation steering.
New research proposes an algorithm for reinforcement learning that achieves asymptotically optimal regret without dependence on the task horizon.
New research proposes a method for learning local causal structures from observational data while addressing latent variables and selection bias.
Research paper proposes advanced AI/ML models for proactive forecasting of time series network utilization KPIs to optimize resource provisioning.
A Good Practice Guide for quantifying uncertainties in machine learning models applied to photoplethysmography (PPG) signals from wearables was published.
Research explored Time Series Foundation Models (TSFMs) like TimesFM, Chronos, and MOIRAI for zero-shot heart rate variability forecasting from consumer wearables.
Research explores using neural collapse instability to prioritize test cases for Deep Neural Networks, aiming to reduce validation costs in critical domains.
Research evaluates nine pre-trained embedding models for gender bias in ML-based recruitment, analyzing inference from unstructured CV text.
Research introduces DynamicRubric, a method to co-evolve LLM evaluators and policies, addressing score-gap collapse in policy optimization.
Research explores autonomous collaborative learning among Tsetlin Machines (TMs) with consensus-based inference, extending distributed TM learning.
Research presents CURED, a web demonstrator unifying ML-based data cleaning and error models for tabular data.
Research frames Active Inference (AIF) as a convex Markov Decision Process, simplifying policy optimization for expected free energy minimization.
Research explores Gaussian averaging as a smooth surrogate for quantized neural networks, deriving bounds on local oscillation for discontinuous models.
Researchers propose a neuromorphic-inspired Receptron model for efficient, non-linear classification at the edge, reducing compute and memory.
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