Time series forecasting plays a crucial role in contemporary engineering information systems for supporting decision-making across various industries, where Rec...
Many real-world software tasks require exact transcription of provided data into code, such as cryptographic constants, protocol test vectors, allowlists, and c...
Machine learning (ML) algorithms are increasingly deployed to make critical decisions in socioeconomic applications such as finance, criminal justice, and auton...
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its stability is fundamentally challenged ...
Autonomous navigation in complex and partially observable environments remains a central challenge in robotics. Several bio-inspired models of mapping and navig...
We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable ski...
Feedforward artificial neural networks (ANNs) trained on static images remain the dominant models of the the primate ventral visual stream, yet they are intrins...
As Byzantine Fault Tolerant (BFT) protocols begin to be used in permissioned blockchains for user-facing applications such as payments, it is crucial that they ...
We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware opt...
Large language models (LLMs) are increasingly being used to evolve solutions to problems in many domains, in a process inspired by biological evolution. However...
The field of emergent communication within multi-agent systems examines how autonomous agents can independently develop communication strategies, without explic...
Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbi...
