arXiv:2607.16612v1 Announce Type: new Abstract: Backpropagation makes training deep networks memory intensive because it must store intermediate activations. Forward-mode methods avoid this cost, but their gradient…
arXiv:2607.16675v1 Announce Type: new Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its…
arXiv:2607.16685v1 Announce Type: new Abstract: Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data. In practice, however,…
arXiv:2607.16725v1 Announce Type: new Abstract: Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant. To…
arXiv:2607.16717v1 Announce Type: new Abstract: This paper proposes a causal independence principle for value -- the value Causal Markov Condition (v-CMC) -- and develops the conceptual…
arXiv:2607.16927v1 Announce Type: new Abstract: We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces. DABS learns…
arXiv:2607.16761v1 Announce Type: new Abstract: Dropout and Random Gradient Masking (RaM) are two training techniques used to improve performance in deep learning. Both techniques inject randomness…
arXiv:2602.04596v2 Announce Type: replace Abstract: Bayes-filtered transformers are transformers meta-learned on sequences from a prior predictive distribution to approximate the corresponding posterior predictive distribution. They output…
arXiv:2607.18225v1 Announce Type: cross Abstract: We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential…
arXiv:2607.11969v2 Announce Type: replace Abstract: Point-adjustment (PA), for years the default scoring protocol in time-series anomaly detection (TSAD), was shown by Kim et al. (2022) to…
arXiv:2605.04344v2 Announce Type: replace Abstract: This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast…
arXiv:2509.08765v4 Announce Type: replace-cross Abstract: Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of…
arXiv:2506.00452v5 Announce Type: replace-cross Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing-based approaches, such as linear minimum mean-squared error…
arXiv:2607.16542v1 Announce Type: new Abstract: Reinforcement learning (RL) is primarily known as a computational method for optimizing control tasks, but it is increasingly used to explain…
arXiv:2408.14917v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms…
arXiv:2510.23472v2 Announce Type: replace-cross Abstract: Chip placement is a vital stage in modern chip design, and black-box optimization (BBO) has been applied to it for decades.…
arXiv:2607.17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and…
arXiv:2507.07247v2 Announce Type: replace-cross Abstract: As large language models (LLMs) and visual language models (VLMs) grow in scale and application, attention mechanisms have become a central…
arXiv:2607.16623v1 Announce Type: new Abstract: In The Algebraic Mind, Marcus identified three cognitive components: operations over variables, recursively structured representations, and an individual/kind distinction. He left…
arXiv:2607.16723v1 Announce Type: new Abstract: Large-scale multi-objective optimization problems (LSMOPs) are challenging due to their high-dimensional decision spaces. Fuzzy search is an effective technique for improving…
arXiv:2607.16876v1 Announce Type: new Abstract: Constrained Optimization Problems are crucial in fields such as engineering, economics, and robotics, where high-dimensional search spaces and complex objectives and…
arXiv:2410.18777v1 Announce Type: cross Abstract: This research presents an online path planner for Unmanned Aerial Vehicles (UAVs) that can handle dynamic obstacles and UAV motion constraints,…
arXiv:2607.17858v1 Announce Type: new Abstract: Reservoir computing exploits nonlinear dynamical systems to encode temporal inputs into high-dimensional state space representations. Although reservoir performance is often characterized…
arXiv:2607.17090v1 Announce Type: cross Abstract: We study the expressivity of shallow polynomial neural networks (PNNs) with monomial activation functions over finite fields. For a given architecture,…