arXiv:2503.05810v3 Announce Type: replace Abstract: The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which present limitations. To address these,…
arXiv:2509.19604v1 Announce Type: new Abstract: Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or engineered independently. While these complex formats can expand disease applicability and…
arXiv:2412.12038v2 Announce Type: replace Abstract: Mixed integer linear programming (MILP) solvers expose hundreds of parameters that have an outsized impact on performance but are difficult to configure for all but expert users. Existing machine learning (ML) approaches require training on…
arXiv:2509.19601v1 Announce Type: new Abstract: In several applications, including in synthetic biology, one often has input/output data on a system composed of many modules, and although the modules’ input/output functions and signals may be unknown, knowledge of the composition architecture…
arXiv:2305.16056v4 Announce Type: replace Abstract: Reinforcement Learning Algorithms are predominantly developed for stationary environments, and the limited literature that considers nonstationary environments often involves specific assumptions about changes that can occur in transition probability matrices and reward functions. Considering that…
arXiv:2509.19586v1 Announce Type: new Abstract: We introduce FragAtlas-62M, a specialized foundation model trained on the largest fragment dataset to date. Built on the complete ZINC-22 fragment subset comprising over 62 million molecules, it achieves unprecedented coverage of fragment chemical space.…
arXiv:2509.20239v1 Announce Type: cross Abstract: This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating the control problem in a general dynamic programming framework, introducing the mathematical structure needed for a detailed…
arXiv:2509.19554v1 Announce Type: new Abstract: This thesis explores how deep learning models learn over time, using ideas inspired by force analysis. Specifically, we zoom in on the model’s training procedure to see how one training example affects another during learning,…
arXiv:2509.20134v1 Announce Type: cross Abstract: The “No Free Lunch” theorem dictates that no single recommender algorithm is optimal for all users, creating a significant Algorithm Selection Problem. Standard meta-learning approaches aim to solve this by selecting an algorithm based on…
arXiv:2509.19538v1 Announce Type: new Abstract: Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforcement learning (RL). However, many existing methods do not directly generate actions alongside states and rewards, limiting their compatibility with standard…
