arXiv:2406.17374v3 Announce Type: replace Abstract: Experimental studies are a cornerstone of Machine Learning (ML) research. A common and often implicit assumption is that the study’s results will generalize beyond the study itself, e.g., to new data. That is, repeating the…
arXiv:2509.21423v2 Announce Type: replace-cross Abstract: We study the problem of causal structure learning from a combination of observational and interventional data generated by a linear non-Gaussian structural equation model that might contain cycles. Recent results show that using mere observational…
arXiv:2512.04135v1 Announce Type: new Abstract: Large Language Diffusion Models (LLDMs) benefit from a flexible decoding mechanism that enables parallelized inference and controllable generations over autoregressive models. Yet such flexibility introduces a critical challenge: inference performance becomes highly sensitive to the…
arXiv:2511.07161v2 Announce Type: replace Abstract: LLMscape is an interactive installation that investigates how humans and AI construct meaning under shared conditions of uncertainty. Within a mutable, projection-mapped landscape, human participants reshape the world and engage with multiple AI agents, each…
arXiv:2403.02514v3 Announce Type: replace-cross Abstract: The rapid advancement of artificial intelligence is enabling the development of increasingly autonomous robots capable of operating beyond engineered factory settings and into the unstructured environments of human life. This shift raises a critical autonomy-alignment…
arXiv:2512.04252v1 Announce Type: new Abstract: Predicting the inhibitory potency of small molecules against Tyrosyl-DNA Phosphodiesterase 1 (TDP1)-a key target in overcoming cancer chemoresistance-remains a critical challenge in early drug discovery. We present a deep learning framework for the quantitative regression…
arXiv:2507.20862v2 Announce Type: replace Abstract: Parkinson Disease (PD) often results in motor and cognitive impairments, including gait dysfunction, particularly in patients with freezing of gait (FOG). Current detection methods are either subjective or reliant on specialized gait analysis tools. This…
arXiv:2512.04198v1 Announce Type: new Abstract: A standard assumption in deep learning is that the inductive bias introduced by a neural network architecture must persist from training through inference. The architecture you train with is the architecture you deploy. This assumption…
arXiv:2512.04223v1 Announce Type: new Abstract: Modelling the complexity and diversity of human activity scheduling behaviour is inherently challenging. We demonstrate a deep conditional-generative machine learning approach for the modelling of realistic activity schedules depending on input labels such as an…
arXiv:2512.04189v1 Announce Type: new Abstract: Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy consumption. These advantages make them particularly well suited for deployment on resource-constrained…
