Energy
DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
This occurs even under stationary data distributions, and its underlying mechanism is poorly understood.
HEMM: Holistic Evaluation of Multimodal Foundation Models Paul Pu Liang
To address this need, we contribute Holistic Evaluation of Multimodal Models ( HEMM), visualized in Figure 1. HEMM, as an evaluation framework, goes beyond conventional lists of datasets to emphasize holistic benchmarking at three levels.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting.
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One such form of structure that is commonly observed in multi-agent scenarios is symmetry.
Differentiable Quantum Computing for Large-scale Linear Control Connor Clayton
In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups.
DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
We also introduce a novel benchmark for evaluating NNPs in molecular property prediction, Hamiltonian prediction, and conformational optimization tasks.
Causal discovery with endogenous context variables
Context-dependent systems have been widely studied; see Mooij et al.
3dbb8b6b5576b85afb3037e9630812dc-Paper-Conference.pdf
Addressing these gaps, we proposed Place3D, a full-cycle pipeline that encompasses Li-DAR placement optimization, data generation, and downstream evaluations.