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4,009 papers
This work proposes LocalProp, a training procedure that locally updates the weights of a model, where the pre-training is based on I-JEPA, and empirically shows that it reaches good performance at a fraction of GPU peak memory.
In tri-modal visual question answering (VQA), auxiliary text is commonly used to complement visual and textual inputs, yet its reliability is often uncontrolled. While prior work studies modality conflicts in general, it remains unclear how different types of unreliable auxiliary text affect answer selection under fixe...
UniSandbox, a decoupled evaluation framework paired with controlled, synthetic datasets to avoid data leakage and enable detailed analysis, reveals a significant understanding-generation gap, which is mainly reflected in two key dimensions: reasoning generation and knowledge transfer.
Ensuring online safety through content monitoring had raised Hate Speech Detection as a crucial task to be addressed. By essence the task demands the capture of contextual cues, which are essential for a precise understanding of the content's intent. Although automated detection approaches for the task have advanced si...
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It is found that nearly identical QA accuracy can mask substantial differences in completion, answer validity, and generation workload, while proactive performance separates into response quality, response delay, false alarms, and missed target windows.
This paper presents provably scale-invariant Gaussian derivative residual networks (GaussDerResNets), constructed out of scale-covariant Gaussian derivative residual blocks coupled in cascade, aimed at addressing the topic of multi-object detection in a provably scale-invariant manner.
LoRA merging methods increasingly operate on the low-rank structure of task updates, yet how the common subspace is estimated and how coefficients are assigned after recomposition are rarely compared directly. We propose CT-Merging, which estimates common directions from averaged task subspace projectors and assigns a...
This paper proposes a natural generalization of PInv to the nonlinear regime in general and to neural networks in particular, and introduces Surjective Pseudo-invertible Neural Networks (SPNN), a class of architectures explicitly designed to admit a tractable non-linear PInv.
DeepFedNAS is introduced, a two-phase framework built on a multi-objective fitness function that synthesizes information-theoretic network metrics with architectural heuristics that makes DeepFedNAS practical for scalable, communication-constrained IoT federations.
This work formalizes Open-Vocabulary Domain Unlearning (OVDU), a rigorous protocol that mandates domain forgetting must transfer to held-out classes and proposes a surgical parameter-editing framework that vastly improves open-vocabulary generalization over existing baselines.
Overall, WIPT provides a streaming-compatible form of test-time prototype adaptation that can improve difficult low-shot cross-domain decisions without target-time optimization.
StarWM is proposed, which uses a cross-attention module trained on self-supervised dynamics to decide where reconstruction applies and preserves state attributes with near-perfect fidelity through long-horizon imagination while systematically discarding distractors.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
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.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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