Machine Learning

Boosting structured representation learning, a novel Discrete Dictionary-based Decomposition (D3) layer significantly improves systematic generalization in TPR-based models by efficiently decomposing …

AI discovers robust, biologically-plausible plasticity rules that self-organize and maintain neural circuits’ sequential activity, even with synaptic turnover.

DUPLEX: a novel RL method trains diverse, near-optimal policies in complex, dynamic environments by explicitly maximizing policy diversity using successor features. It outperforms existing methods in…

DISCEDIT efficiently identifies and edits discriminative neural network components for structured pruning and class unlearning, achieving high sparsity and forgetting rates without needing training da…

DIGRAF, a novel graph-adaptive activation function, significantly boosts Graph Neural Network performance by dynamically adapting to graph structure, offering consistent superior results across divers…

DiffusionPDE uses generative diffusion models to solve PDEs accurately, even with highly incomplete observations, outperforming state-of-the-art methods.

Diffusion-Reward Adversarial Imitation Learning (DRAIL) enhances Generative Adversarial Imitation Learning by integrating diffusion models, resulting in more stable and smoother reward functions for s…

Diffusion-DICE: A novel offline RL method using in-sample diffusion guidance for optimal policy transformation, achieving state-of-the-art performance.

QVPO, a novel online RL algorithm, leverages diffusion models’ multimodality to boost performance in continuous control tasks, overcoming limitations of unimodal policies.

DiCuRL uses diffusion models to generate challenging yet achievable RL training curricula, outperforming nine state-of-the-art methods.

Twigs: a novel score-based diffusion framework using multiple co-evolving flows and loop guidance for superior conditional graph generation.

Diff-Tuning: a simple yet effective approach transfers pre-trained diffusion models to various downstream tasks by leveraging the ‘chain of forgetting’ phenomenon, improving transferability and conver…

Diffusion Spectral Representation (Diff-SR) enables efficient reinforcement learning by extracting sufficient value function representations from diffusion models, bypassing slow sampling and facilita…

DACER, a novel online RL algorithm, uses diffusion models to learn complex policies and adaptively balances exploration-exploitation via entropy estimation, achieving state-of-the-art performance on M…

DiffPO: A causal diffusion model learns outcome distributions, offering reliable medical interventions.

Boost classifier robustness with DiffAug, a novel diffusion-based augmentation method! One forward and reverse diffusion step enhances robustness against covariate shifts, adversarial examples, and o…

DFA-GNN: A novel forward learning framework for GNNs enhances training efficiency and robustness by directly aligning feedback signals, outperforming traditional methods.

MOMBO: a novel offline reinforcement learning algorithm that uses deterministic uncertainty propagation for faster convergence and tighter suboptimality bounds.

This paper presents an efficient algorithm to compute near-optimal deterministic policies for constrained reinforcement learning problems, solving a 25-year-old computational complexity challenge.

Derivative-enhanced DeepONets boost PDE solution accuracy and derivative approximation, particularly valuable with limited training data.