WA-JEPA is presented, a V-JEPA-native world-action model designed for autonomous driving planning that employs hybrid future-masked pre-training and recast future prediction as conditional flow matching over latent futures, which substantially improves the model's ability to generate plausible future latents for downst...
SaMer is proposed, an object-aware token merging framework that compresses image-side post-projector tokens into representative centroids while preserving the original late-interaction interface, and outperforms compression baselines and shows stronger phrase-level grounding, suggesting that efficient multi-vector retr...
Suhyeong Park, Junha Jung, Jungwoo Park et al.· arXiv.org· 1 citation
This work characterises a fundamental phenomenon in models trained on imbalanced data, termed the preference issue, wherein models exhibit higher training error and a larger generalisation gap for classes with limited data and demonstrates that CBS effectively mitigates the preference issue.
Yi-Hang Zhu, Rajeev Raman, Shiqi Su et al.· arXiv.org· 0 citations
This work proposes reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field and provides a general framework that recovers recent fine-tuning methods, including RAM and DiffusionNFT, as special cases.
Jaemoo Choi, Wei Guo, Yuchen Zhu et al.· 1 citation
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This work instantiates FBA for coastal harbor understanding, a representative multi-source scenario, by constructing CPRS (Coastal-Port Remote Sensing), a three-layer supervision dataset coupled with three ordered stages: domain-bridge convergence for shared RS priors across target and bridging scenarios under differen...
PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...
Shuaiyan Li, Xiaorui Wang, Yuchen Zhu et al.· Journal of Chemical Informat...· 4 citations
This study proposes SynGFN, which models molecular design as a cascade of simulated chemical reactions, enabling the assembly of molecules from synthesizable building blocks, as a bridge linking molecular design and synthesis, accelerating exploration and producing diverse, synthesizable, high-performance molecules.
Results indicate that BF3 antagonists can synergize with the LBP antagonist to amplify conformational flexibility in H12 and induce anticorrelated dynamics of H12 with H3 and H4, suggesting a promising avenue for enhancing the efficacy and overcoming the resistance in castration-resistant prostate cancer treatment.
Xiaotian Kong, Yushan Zou, Peng Cao et al.· Journal of Chemical Informat...· 1 citation
This work introduces an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision, and improves the model's accuracy, generalization, and robustness in complex scenarios.
Qiaolin Gou, Qun Su, Ji-Ke Wang et al.· Journal of Chemical Informat...· 1 citation
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.· Journal of Chemical Informat...· 13 citations· ⚡1
This study established a unified model (NRIGN) based on the deep graphic architecture to discriminate agonists and antagonists targeting 26 successful or in-clinical-trial NR targets and achieves an excellent prediction accuracy and is robust enough to be applied in various real-world scenarios.
Kaimo Yang, Dejun Jiang, Qirui Deng et al.· Journal of Chemical Informat...· 2 citations
This study presents ECloudGen, which uses latent diffusion to generate electron clouds from protein pockets and decodes them into molecules, and adopts two-stage training, which expands the chemical space accessible to generative drug design.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
