arXiv:2106.06998v3 Announce Type: replace Abstract: Training convolutional neural networks at scale demands substantial memory, largely due to storing intermediate activations for backpropagation. Existing approaches — such as checkpointing, invertible architectures, or gradient approximation methods like randomized automatic differentiation — either…
arXiv:2603.08913v1 Announce Type: new Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, regulatory element identification, and cross-task transfer learning. However, as these models are increasingly trained or…
arXiv:2505.11635v2 Announce Type: replace Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous latent models can struggle to express. We introduce the Gaussian-Multinoulli Restricted Boltzmann Machine (GM-RBM), a generative energy-based model…
arXiv:2603.08914v1 Announce Type: new Abstract: Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that randomly initialized networks contain sparse subnetworks achieving competitive accuracy without weight training. Existing SLT methods,…
arXiv:2509.06067v2 Announce Type: replace Abstract: Finite element methods (FEM) for high-temperature superconducting (HTS) magnets become time-consuming at larger scales, restricting the rapid optimization of meter-scale REBCO solenoids. In this work, a surrogate model based on a fully connected residual neural…
arXiv:2603.08960v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models deliver high quality at low training FLOPs, but this efficiency often vanishes at inference. We identify a double penalty that structurally disadvantages MoE architectures during decoding: first, expert routing fragments microbatches and…
arXiv:2601.22444v2 Announce Type: replace Abstract: Forecasting future events is highly valuable in decision-making and is a robust measure of general intelligence. As forecasting is probabilistic, developing and evaluating AI forecasters requires generating large numbers of diverse and difficult questions, and…
arXiv:2603.08965v1 Announce Type: new Abstract: AI memory systems increasingly organize knowledge into graph structures — knowledge graphs, entity relations, community hierarchies — yet lack a principled mechanism for continuous resolution control: where do the qualitative boundaries between abstraction levels lie,…
arXiv:2603.07313v2 Announce Type: replace Abstract: Robustness under latent distribution shift remains challenging in partially observable reinforcement learning. We formalize a focused setting where an adversary selects a hidden initial latent distribution before the episode, termed an adversarial latent-initial-state POMDP. Theoretically,…
arXiv:2603.08972v1 Announce Type: new Abstract: Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to continuous learning while dealing with concept drifts, temporal dependence, and…
