Machine Learning

Neuronal Competition Groups (NCGs) enhance supervised STDP training in spiking neural networks by promoting balanced competition and improved class separation, resulting in significantly higher classi…

NeuralSolver: A novel recurrent solver efficiently and consistently extrapolates algorithms from smaller problems to larger ones, handling various problem sizes.

NeuralFuse: A novel add-on module learns input transformations to maintain accuracy in low-voltage DNN inference, achieving up to 57% accuracy recovery and 24% energy savings without retraining.

Neural P³M enhances geometric GNNs by incorporating mesh points to model long-range interactions in molecules, achieving state-of-the-art accuracy in predicting energy and forces.

Neural Flow Diffusion Models (NFDM) revolutionize generative modeling by introducing a learnable forward process, resulting in state-of-the-art likelihoods and versatile generative dynamics.

Neural Embeddings Rank (NER) aligns 3D latent neural dynamics with movements, enabling cross-session decoding and revealing consistent neural dynamics across brain areas.

Scalable SEEG decoding model, seegnificant, leverages transformers to decode behavior across subjects despite electrode variability, achieving high accuracy and transfer learning capability.

Neural Conditional Probability (NCP) offers a new operator-theoretic approach for efficiently learning conditional distributions, enabling streamlined inference and providing theoretical guarantees fo…

Researchers enhance imbalanced data classification by inducing Neural Collapse to Multiple Centers (NCMC) using a novel cosine loss function, achieving performance comparable to state-of-the-art metho…

Neural Collapse-inspired Feature Alignment (NCFAL) significantly boosts out-of-distribution generalization by aligning semantic features to a simplex ETF, even without environment labels.

Researchers introduce Geometric Parameterization (GmP), a novel neural network parameterization resolving instability in ReLU network training, leading to faster convergence and better generalization.

Neuc-MDS: Revolutionizing multidimensional scaling by using bilinear forms for non-Euclidean data, minimizing errors, and resolving the dimensionality paradox!

Contrastive Divergence algorithms achieve near-optimal parameter estimation rates, matching the Cramér-Rao lower bound under specific conditions, as proven by a novel non-asymptotic analysis.

Near-optimal dynamic regret is achieved for adversarial linear mixture MDPs with unknown transitions, bridging occupancy-measure and policy-based methods for superior performance.

This paper presents near-optimal sample complexity bounds for solving distributionally robust reinforcement learning problems with general Lp norms, showing robust RL can be more sample-efficient than…

SVOGS: Near-optimal distributed minimax optimization is achieved under second-order similarity, balancing communication, computation, and achieving near-optimal complexities.

New distributional RL algorithm (DCFP) achieves near-minimax optimality for return distribution estimation in the generative model regime.

ParamRepulsor, a novel parametric dimensionality reduction method, achieves state-of-the-art local structure preservation by mining hard negatives and using a tailored loss function.

ChemFlow: a new framework efficiently explores chemical space using latent flows, unifying existing methods & incorporating physical priors for molecule manipulation and optimization.

New algorithm, POAM, excels at multi-agent cooperation by adapting to diverse and changing teammates in dynamic scenarios.