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20,463 papers
It is argued that continuous-time graph learning should identify low-dimensional states sufficient for future prediction rather than reconstruct the full history, and proposed PreDyG, a lightweight model whose capacity is matched to the intrinsic predictive dimension.
Real-time video generation, interactive world models, and on-device image synthesis are almost a reality. Diffusion Transformers (DiT) achieve excellent sample quality, shifting the bottleneck to the throughput of the models themselves. Natural images are inherently sparse, as large portions of an image are often backg...
PULSE, an event-driven framework for personalized opportunistic learning that treats each encounter as an online collaboration decision, selecting either no interaction or a promising nearby peer, shows that selective opportunistic collaboration can provide effective personalization while limiting communication and res...
Multilingual evaluations often score a model's opening yes or no, a token that also carries model and language answering conventions. We ask when different ways of eliciting and reading an answer recover the same judgment, and when they measure an answer habit. We collect 1.1 million replies from eight models in six la...
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Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health. Previous deterministic hazard index assessments provide limited treatment of uncertainty, co-exposure, and spatial variability. This study applies a Bayesian probabilistic framework f...
Concept Bottleneck Models (CBMs) promise predictions that humans can inspect and correct through concepts such as"curved beak". Yet on CUB-200-2011, their standard benchmark, progress is measured like that of ordinary classifiers: by task and concept accuracy. Neither reveals whether the label is predicted from the exp...
A causal, training-free linker that allows a frozen streaming diarizer to track more speakers than it has output slots and only relabels, so missed speech and false alarm are identical to the host's and every gain is a reduction in speaker confusion.
A compact explainability-driven framework for comparing encoders trained with classification-only and caption-generation supervision on CUB-200 provides a systematic basis for assessing learned representations and comparing alternative training and transfer-learning strategies.
A diagnostic study finds that stronger teacher--student agreement in intermediate transitions is associated with higher answer correctness within questions, with correct responses exhibiting lower mean transition KL across all measured intermediate layer pairs.
The proposed HuTact framework, a framework that pretrains tactile-aware hand representations from human HOI data without requiring real tactile signals during pretraining, suggests that human interaction data can serve as a scalable complementary source for robotic tactile representation learning.
This work proposes ReCam, which groups local spatiotemporal blocks of historical latents into vectors and projects them directly into the high-dimensional hidden space of a Diffusion Transformer, which learns to use the resulting memory tokens through native self-attention, reducing token count while retaining block-le...
Automated respiratory sound classification enables continuous, non-invasive respiratory assessment, yet long-term deployment remains challenging due to domain shifts from evolving recording devices and acquisition periods. When privacy and governance regulations prohibit replaying historical data, sequential learning e...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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