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9 papers · 2 filters
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi +7
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euc…
Stochastic Average Gradient : A Simple Empirical Investigation
Pascal Junior Tikeng Notsawo
Despite the recent growth of theoretical studies and empirical successes of neural networks, gradient backpropagation is still the most widely used algorithm for training such netw…
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
Mohammad-Javad Darvishi-Bayazi, Mohammad Sajjad Ghaemi, Timothee Lesort +3
Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligen…
Worrisome Properties of Neural Network Controllers and Their Symbolic Representations
Jacek Cyranka, Kevin E M Church, Jean-Philippe Lessard
We raise concerns about controllers' robustness in simple reinforcement learning benchmark problems. We focus on neural network controllers and their low neuron and symbolic abstra…
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar, Sumner Magruder, Edward De Brouwer +4
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for und…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…