arXiv:2507.09768v3 Announce Type: replace Abstract: In recent years, deep learning-based single-channel speech separation has improved considerably, in large part driven by increasingly compute- and parameter-efficient neural network architectures. Most such architectures are, however, designed with a fixed compute and parameter…
arXiv:2603.03491v1 Announce Type: new Abstract: Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottleneck. However, their reliance on emerging non-volatile memory devices introduces device-level non-idealities-such as write…
arXiv:2510.14936v2 Announce Type: replace Abstract: The fields of explainable AI and mechanistic interpretability aim to uncover the internal structure of neural networks, with circuit discovery as a central tool for understanding model computations. Existing approaches, however, rely on manual inspection…
arXiv:2603.03507v1 Announce Type: new Abstract: Adversarial attacks – input perturbations imperceptible to humans that fool neural networks – remain both a persistent failure mode in machine learning, and a phenomenon with mysterious origins. To shed light, we define and analyze…
arXiv:2601.17204v3 Announce Type: replace Abstract: Small-molecule identification from tandem mass spectrometry (MS/MS) remains a bottleneck in untargeted settings where spectral libraries are incomplete. While deep learning offers a solution, current approaches typically fall into two extremes: explicit generative models that…
arXiv:2603.03511v1 Announce Type: new Abstract: We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic wavefunctions of a molecule evolve over…
arXiv:2404.03813v5 Announce Type: replace-cross Abstract: We define a quantum learning task called agnostic tomography, where given copies of an arbitrary state $rho$ and a class of quantum states $mathcal{C}$, the goal is to output a succinct description of a state…
arXiv:2603.03517v1 Announce Type: new Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introducing reasoning tokens does not yield…
arXiv:2506.18703v3 Announce Type: replace-cross Abstract: Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition. When using appropriate modeling units, e.g., byte-pair encoding, these systems are in principle open vocabulary systems. In practice, however, they often fail to recognize words…
arXiv:2603.03523v1 Announce Type: new Abstract: We study reinforcement learning in infinite-horizon discounted Markov decision processes with continuous state spaces, where data are generated online from a single trajectory under a Markovian behavior policy. To avoid maintaining an infinite-dimensional, function-valued estimate,…
