Category
3,367 papers
The experiments show that CATTLE can learn generalized context from a single source data set and is rank-wise and statistically superior to nine state-of-the-art baselines, including machine learning, deep learning, and transfer learning methods using large-scale pre-trained models.
HARTS is the first system to demonstrate arbitrary-rollout-tree prefix-sharing speedups on a real hybrid-attention model, and its numerical differences are comparable to baseline self-rerun variation, and its reward trend is similar to the baseline over the first 120 steps of SWE-bench training.
A generative modeling framework for characterizing EV energy consumption under real-world operating conditions, providing an essential foundation for uncertainty-aware fleet planning in large-scale operational settings is demonstrated.
AdaRDiff is proposed, a generalized differencing approach that uses learnable weights to simplify the series through weighted differencing with previous time instants, and attains state-of-the-art forecast accuracy across eight benchmarks spanning electricity, weather, traffic, and energy, at negligible parameter cost.
Reach audiences
Advertise in front of researchers, engineers, and readers.
Generalized Gibbs Ensemble Weighting is developed, a probabilistic framework that treats forecasting models as experts and assigns ensemble weights using a Gibbs-style exponential transformation of normalized predictive loss and produces a family of related methods, including Stable Gibbs weighting, Directional Gibbs-NCL, and Symmetric Gibbs-NCL.
A systematic comparison of four classical machine learning architectures, support vector machines, artificial neural networks, convolutional neural networks, and long short-term memory networks against their quantum counterparts against their quantum counterparts characterize the trade-offs between classical and quantum approaches under realistic, resource-constrained conditions and provide a benchmark for future studies on actual quantum hardware.
Hanneke, Moran, Shlimovich and Yehudayoff (COLT 2025) posed the following open problem. A selector sees a finite dataset $D \subseteq \mathbb{R}^d \times \mathbb{R}$, picks at most $n$ examples together with nonnegative weights, and hands the weighted least squares objective to the minimum-norm ERM. Writing $F_w(d,n)$ for the worst-case ratio between the loss of the returned predictor on all of $D$ and the optimal loss, they proved $F_w(d,n)=\infty$ for $n<d$, $F_w(d,d)=d+1$ and $F_w(d,n)=1$ for $n \ge 2d$, and asked for the value in the open regime $d<n<2d$. We determine this value in several cases. For every $d$ we prove $F_w(d,2d-1)=1+1/d$, which confirms a claim stated without proof in the original note. We further prove $F_w(3,4)=5/3$ and $F_w(4,5)=2$, the two smallest cells not covered by the endpoint formula. For every intermediate budget $n=d+k$ we prove the lower bound $F_w(d,d+k) \ge 1+\Gamma_{d,k}$, where $\Gamma_{d,k}$ is an explicit harmonic quantity over balanced partitions, and we show that this bound is the exact minimax value over the class of datasets whose whitened gradient systems carry an orthogonal circuit-block structure. All three exact values match $1+\Gamma_{d,k}$, and we conjecture that equality holds throughout the open regime. The upper bound proofs run on a common geometric spine: a rigidity theorem for positive spanning configurations of loss gradients, classifications and structural reductions of small positive bases in $\mathbb{R}^3$ and $\mathbb{R}^4$, and a dimension-free extremal-basis argument that converts sign-cone geometry into five-point selections. We also give explicit counterexamples showing that several shorter routes fail, and constructive polynomial-time selection algorithms for all proved cases.
This note argues that, at the level of trajectory generation and action-value updating, the distinction between Monte Carlo Tree Search and MC control is largely terminological.
Experiments show that PhyMamba achieves the best aggregated performance, with an overall mean error reduction of 31.8% compared with a diverse range of baselines, which supports practical deployment for robust battery health prognostics.
A novel framework of temporal memory-aware Online Test-Time Adaptation on Dynamic Graphs, named DGOTTA, to effectively adapt well-trained DGNNs during test time and significantly improves generalization under diverse distribution shifts and multiple model architectures is proposed.
A novel framework to estimate parameters for reproducing target multicellular patterns using an agent-based model (ABM) and outperforms conventional methods such as PointNet++ is proposed.
Multimodality translation (e.g., text-to-image) is a core generative AI task. However, existing approaches (1) follow generative paths that do not directly represent the source modality, limiting the flexibility of some sampling algorithms; and (2) are unidirectional, preventing inversion (e.g., image-to-text). We propose BIT: Bidirectional Image-Text Diffusion Bridges. In contrast to previous approaches, BIT starts directly from text and interpolates into images, providing (1) a source-aware generative path that enables diverse and flexible sampling algorithms; and (2) an endpoint-conditioned process that can be traversed from image to text, providing a unified, bidirectional generative framework. BIT is derived through stochastic calculus, yielding SDE forms amenable to simulation and tractable loss functions that scale to high dimensions. Our experiments show that BIT is competitive with denoising-diffusion and deterministic-flow baselines, and outperforms them on several vision--language and natural-science evaluations.
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
