Category
2,173 papers
Open-MOPD, a principled framework incorporating token-share balancing, gap-aware dynamic budget allocation, and student reward refresh, systematically restore cross-domain balance, elevating headroom recovery from 35.6% to 83.4% in a single deployable student.
We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks. In their standard form, BVNs follow the principle of quantum Fourier sampling: labelled data are placed in superposition and interfered in the Fourier basis to extract globally informative features. We then define generalised BVNs that enable interference in problem-adapted bases, yielding more expressive models under the same measurement budget as in the standard setting. BVNs achieve universal function approximation through (over)complete interference bases, while training of BVNs is gradient-free. Experiments on synthetic and real-world classification tasks, as well as implicit image representation, show strong generalisation capabilities and competitive performance with classical and quantum baselines.
Harness Continual Learning is formulated, a new continual learning paradigm in which the harness evolves around a frozen foundation model, and the resulting loss of earlier behavior as harness-level forgetting is defined.
This work introduces Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions and establishes Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.
Reach audiences
Advertise in front of researchers, engineers, and readers.
A graphical notation for designing interpretable AI architectures, adapted from Penrose tensor notation is introduced, which gives a global view of an architecture and maps one to one onto PyTorch einsum code.
The proposed Module Level Reward Evolution Framework integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback, which together improve the effectiveness and robustness of reward optimization.
This paper investigates how multilingual medical adaptation reshapes the internal representations of Whisper models through layer-wise encoder analysis, and shows that English medical fine-tuning produces the dominant encoder shift, whereas multilingual continuation largely preserves the adapted representation space.
The results suggest that the empirical success of memory replay goes beyond the mitigation of forgetting, actively reintroducing the benefits of data co-observation into the learning process.
The Explanation Consistency Score (ECS) is introduced, a fairness-aware metric based on Jensen-Shannon divergence that quantifies the similarity of attribution maps across subgroups that suggests predictive fairness and explanation consistency capture complementary dimensions of model behavior, motivating fairness evaluations that extend beyond predictive performance.
Flama is presented, an open-source Python framework for developing and deploying production-ready web APIs, machine learning services, and large-language-model (LLM) applications that unifies REST API development, predictive model serving, and generative AI inference in one architecture.
Improvements show that CutMix has only a minor impact on segmentation accuracy but consistently improves the reliability, particularly under distribution shifts, indicating that CutMix primarily enhances the trustworthiness of the model’s calibration and uncertainty rather than the raw segmentation prediction itself.
This is the first systematic evaluation of UQ methods applied to a foundation model for semantic segmentation and highlights both the promise and the current limitations of uncertainty-aware foundation models, pointing to the need for future work that jointly optimizes accuracy, robustness, and efficiency for real-world deployment.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
