10 accepted papers
Effective dexterous manipulation hinges on the dynamic integration of visual context with fine-grained tactile feedback. This remains a significant challenge, as existing methods often rely on static fusion strategies and struggle to learn from isolated tactile features. To address this, we propose
Diffusion language models (DLMs) can match or surpass similarly sized autoregressive language models on language understanding and reasoning. However, their mask-and-denoise pretraining relies on heuristic random masking, which fails to target the most informative tokens. Consequently, the model spe…
Time-Dependent Differential Equations (TDDEs) model dynamical processes across science and engineering, but time-critical applications require solvers delivering high-fidelity trajectories under stringent latency constraints. Most existing TDDE solvers are limited by time discretization, forcing a l…
Function learning forms the foundation of numerous scientific and engineering tasks. While modern machine learning (ML) methods model complex functions effectively, their escalating complexity and computational demands pose challenges to efficient deployment. In contrast, natural dynamical systems e…
In this work, we propose a novel Deep-Shallow Bidirectional Transformer Interactive Attention Network (DS-BTIAN) designed for robust multimodal emotion recognition. DS-BTIAN leverages pre-trained Wav2Vec2.0 and BERT models for efficient feature extraction without the need for finetuning, enhancing r…
The training of large language models (LLMs) faces significant computational cost challenges, limiting their scalability toward artificial general intelligence (AGI) and broader adoption. With model sizes doubling approximately every 3.4 months and training costs escalating from 64 million USD for G…
The rapid development of AI highlights the pressing need for sustainable energy, a critical global challenge for decades. Nuclear fusion, generally seen as a promising solution, has been the focus of intensive research for nearly a century, with investments reaching hundreds of billions of dollars.…
Time-series modeling is broadly adopted to capture underlying patterns present in historical data, allowing prediction of future values. However, one crucial aspect of such modeling is often overlooked: in highly dynamic environments, data distributions can shift drastically within a second or less.…
Chatbots powered by large language models (LLMs) offer natural, human-like interactions. However, traditional text-to-speech (TTS) models paired with LLMs typically wait for the entire sentence to be generated before starting synthesis, leading to increased response latency. Although word-by-word sp…
Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the…