Deep neural network models degrade significantly in the long-tailed data distribution, with the overall training data dominated by a small set of classes in the head, and the tail classes obtaining less training examples. Addressing the imbalance in the classes, attention in the related literature was given mainly to the adjustments carried out in the decision space in terms of either corrections performed at the logit level in order to compensate class-prior bias, with the least attention to […]

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arXiv:2602.07197v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. Due to the nature of Machine Learning as a Service (MLaaS) applications, black-box defenses are more practical than white-box methods, yet existing purification techniques suffer from key limitations: a lack of justification for specific transformations, dataset dependency, high computational overhead, and a neglect of frequency-domain transformations. This paper conducts a preliminary study on various image transformations, identifying down-upscaling as the most effective backdoor trigger […]

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arXiv:2601.16312v1 Announce Type: new Abstract: Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs prove ineffective on this problem space because: (i) most models lack polymer-specific knowledge (ii) existing aligned models lack coverage of knowledge and capabilities relevant to polymer design. Addressing this, we introduce PolyBench, a large scale training and test benchmark dataset of more than 125K polymer design related tasks, leveraging a knowledge base of 13M+ data points obtained […]

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Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor […]

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arXiv:2604.21260v1 Announce Type: new Abstract: We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction-Powered Inference, which post-hoc calibrates the prediction score on the labeled […]

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What Healthy On-Call Looks Like Incident response gets most of the attention, but teams rarely measure the human cost of on-call with the same rigor they apply to incidents. Phone charged. Laptop by my bed. The distinctive PagerDuty alarm goes off in the middle of the night, and the process begins. I sit up, open my screen to a burst of blinding light, and try to figure out: is this transient, or is something actually broken? I pull […]

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arXiv:2602.20222v1 Announce Type: new Abstract: The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web contexts, with no attention given to its implementation in mobile applications. No work has systematically studied the […]

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Major conference acceptance has become pretty much random and review quality is constantly dropping. ​There is always that one reviewer who understood nothing but still rejects the paper because you didn’t cite “X” or compare with “Y”, and the meta-reviewer usually just goes along with it. In your opinion, is there a conference or journal with a solid review process that is even slightly less random than the others? submitted by /u/kostaspap90 [link] [comments]

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Author(s): Mukundan Sankar Originally published on Towards AI. I built a Streamlit SQL coach that grades your queries and fixes your form without giving away the answer. I use SQL a lot. I made this app to help me when my queries are wrong. I didn’t build it because I’m perfect at SQL. Photo by Rubaitul Azad on UnsplashThe author describes the motivation and development of an AI app designed to enhance SQL training, likening the training process […]

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