Academic
arXiv:2602.21390v1 Announce Type: new Abstract: We study the problem of efficiently producing, in an online fashion, generative models of scalar, multiclass, and vector-valued outcomes that …
arXiv:2602.21408v1 Announce Type: new Abstract: Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian …
arXiv:2602.21415v1 Announce Type: new Abstract: Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available …
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent …
arXiv:2602.21426v1 Announce Type: new Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. …
arXiv:2602.21429v1 Announce Type: new Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. …
arXiv:2602.21441v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not …
arXiv:2602.21442v1 Announce Type: new Abstract: The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms …
arXiv:2602.21454v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) can be interpreted as discrete-time state-space models, where the state evolution corresponds to an infinite-impulse-response (IIR) …
arXiv:2602.21462v1 Announce Type: new Abstract: We describe extensive numerical experiments assessing and quantifying how classifier performance depends on the quality of the training data, a …
arXiv:2602.21466v1 Announce Type: new Abstract: $E(3)$-equivariant neural networks have proven to be effective in a wide range of 3D modeling tasks. A fundamental operation of …
arXiv:2602.21467v1 Announce Type: new Abstract: A key challenge in artificial intelligence and neuroscience is understanding how neural systems learn representations that capture the underlying dynamics …
