传感融合

Magnetic confinement fusion may one day provide reliable, carbon-free energy, but the field currently faces technical hurdles. In this position paper, we highlight six key research challenges in the field of fusion energy that we believe should be research priorities for the Machine Learning (ML) community because they are especially ripe for ML applications: (1) disruption prediction, (2) simulation and dynamics modeling (3) resolving partially observed data, (4) improving controls, (5) guiding experiments with optimal design, and (6) enhancing materials discovery. For each problem, we give background, review past ML work, suggest features of future models, and list challenges and idiosyncrasies facing ML development. We also discuss ongoing efforts to update the fusion data ecosystem and identify opportunities further down the line that will be enabled as fusion and its data infrastructure advance. It is our position that fusion energy offers especially exciting opportunities for ML practitioners to impact decarbonization and the future of energy.

While VideoQA Transformer models demonstrate competitive performance on standard benchmarks, the reasons behind their success are not fully understood. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Or are they achieving high scores by exploiting biases and spurious features? Hence, to provide insights, we design *QUAG* (QUadrant AveraGe), a lightweight and non-parametric probe, to conduct dataset-model combined representation analysis by impairing modality fusion. We find that the models achieve high performance on many datasets without leveraging multimodal representations. To validate QUAG further, we design *QUAG-attention*, a less-expressive replacement of self-attention with restricted token interactions. Models with QUAG-attention achieve similar performance with significantly fewer multiplication operations without any finetuning. Our findings raise doubts about the current models' abilities to learn highly-coupled multimodal representations. Hence, we design the *CLAVI* (Complements in LAnguage and VIdeo) dataset, a stress-test dataset curated by augmenting real-world videos to have high modality coupling. Consistent with the findings of QUAG, we find that most of the models achieve near-trivial performance on CLAVI. This reasserts the limitations of current models for learning highly-coupled multimodal representations, that is not evaluated by the current datasets.

Real-world data generally follows a long-tailed distribution, which makes traditional high-performance training strategies unable to show their usual effects. Various insights have been proposed to alleviate this challenging distribution. However, some observations indicate that models trained on long-tailed distributions always show a trade-off between the performance of head and tail classes. For a profound understanding of the trade-off, we first theoretically analyze the trade-off problem in long-tailed learning and creatively transform the trade-off problem in long-tailed learning into a multi-objective optimization (MOO) problem. Motivated by these analyses, we propose the idea of strategy fusion for MOO long-tailed learning and point out the potential conflict problem. We further design a Multi-Objective Optimization based Strategy Fusion (MOOSF), which effectively resolves conflicts, and achieves an efficient fusion of heterogeneous strategies. Comprehensive experiments on mainstream datasets show that even the simplest strategy fusion can outperform complex long-tailed strategies. More importantly, it provides a new perspective for generalized long-tailed learning. The code is available in the accompanying supplementary materials.

Multimodal fusion is crucial in joint decision-making systems for rendering holistic judgments. Since multimodal data changes in open environments, dynamic fusion has emerged and achieved remarkable progress in numerous applications. However, most existing dynamic multimodal fusion methods lack theoretical guarantees and easily fall into suboptimal problems, yielding unreliability and instability. To address this issue, we propose a Predictive Dynamic Fusion (PDF) framework for multimodal learning. We proceed to reveal the multimodal fusion from a generalization perspective and theoretically derive the predictable Collaborative Belief (Co-Belief) with Mono- and Holo-Confidence, which provably reduces the upper bound of generalization error. Accordingly, we further propose a relative calibration strategy to calibrate the predicted Co-Belief for potential uncertainty. Extensive experiments on multiple benchmarks confirm our superiority. Our code is available at https://github.com/Yinan-Xia/PDF.

Training deep neural networks for large language models (LLMs) remains computationally very expensive. To mitigate this, network growing algorithms offer potential cost savings, but their underlying mechanisms are poorly understood. In this paper, we propose a theoretical framework using backward error analysis to illuminate the dynamics of mid-training network growth. Furthermore, we introduce Deep Fusion, an efficient network training approach that leverages pre-trained initializations of smaller networks, facilitating network growth from diverse sources. Our experiments validate the power of our theoretical framework in guiding the optimal use of Deep Fusion. With carefully optimized training dynamics, Deep Fusion demonstrates significant reductions in both training time and resource consumption. Importantly, these gains are achieved without sacrificing performance. We demonstrate reduced computational requirements, and improved generalization performance on a variety of NLP tasks and T5 model sizes.

Unified multi-model representation spaces are the foundation of multimodal understanding and generation. However, the billions of model parameters and catastrophic forgetting problems make it challenging to further enhance pre-trained unified spaces. In this work, we propose FreeBind, an idea that treats multimodal representation spaces as basic units, and freely augments pre-trained unified space by integrating knowledge from extra expert spaces via ``space bonds". Specifically, we introduce two kinds of basic space bonds: 1) Space Displacement Bond and 2) Space Combination Bond. Based on these basic bonds, we design Complex Sequential & Parallel Bonds to effectively integrate multiple spaces simultaneously. Benefiting from the modularization concept, we further propose a coarse-to-fine customized inference strategy to flexibly adjust the enhanced unified space for different purposes. Experimentally, we bind ImageBind with extra image-text and audio-text expert spaces, resulting in three main variants: ImageBind++, InternVL_IB, and InternVL_IB++. These resulting spaces outperform ImageBind on 5 audio-image-text downstream tasks across 9 datasets. Moreover, via customized inference, it even surpasses the advanced audio-text and image-text expert spaces. Our code and checkpoints are released at https://github.com/zehanwang01/FreeBind