Proposes range-based precision and recall metrics with customizable parameters, improving evaluation of time series anomaly detection.

Nesime Tatbul, Tae Jun Lee, Stan Zdonik et al.

Introduced Reptile, a first-order meta-learning algorithm, excelling on Mini-ImageNet.

Alex Nichol, Joshua Achiam, John Schulman

A DQN-based MODRL framework supports single/multi-policy and linear/nonlinear selection, recovering Pareto solutions on two benchmark tasks.

Thanh Thi Nguyen, Ngoc Duy Nguyen, Peter Vamplew et al.

Introduces tensor field neural networks with SO(3) equivariance for 3D point clouds, using spherical harmonic filters, achieving rotation, translation, and permutation invariance.

Nathaniel Thomas, Tess Smidt, Steven Kearnes et al.

Proposed new preference elicitation strategies based on ranking and clustering to enhance multi-objective decision support.

Luisa M Zintgraf, Diederik M Roijers, Sjoerd Linders et al.

Horovod uses ring-allreduce for efficient GPU communication, reducing code changes and speeding up distributed TensorFlow training.

Alexander Sergeev, Mike Del Balso

Learn deep disentangled embeddings using F-statistic loss to enhance few-shot learning performance.

Karl Ridgeway, Michael C. Mozer

Introduces a compression-based generalization bound for deep nets leveraging noise stability, outperforming naive parameter counting.

Sanjeev Arora, Rong Ge, Behnam Neyshabur et al.

L4 loss-based stepsize adapts dynamically, improving Adam and Momentum optimizers across multiple architectures.

Michal Rolinek, Georg Martius

Proposes Tree SHAP, an efficient algorithm for consistent feature attribution in tree models, reducing complexity from exponential to polynomial time.

Scott M. Lundberg, Gabriel G. Erion, Su-In Lee

ENAS uses parameter sharing with a controller RNN to search neural architectures efficiently, reducing GPU time by 1000x, achieving CIFAR-10 error of 2.89%.

Hieu Pham, Melody Y. Guan, Barret Zoph et al.

MotifNet employs motif-based multivariate polynomial filters to handle directed graphs, outperforming spectral methods on node classification tasks.

Federico Monti, Karl Otness, Michael M. Bronstein

This study demonstrates that fixing most parameters in deep CNNs and training only a small subset achieves near state-of-the-art performance.

Amir Rosenfeld, John K. Tsotsos

Proposed NoDeF, a nonparametric delayed feedback model, improves CVR prediction by capturing complex delay distributions.

Yuya Yoshikawa, Yusaku Imai

MINOS is a high-speed multimodal indoor simulator leveraging large datasets to evaluate deep RL navigation in complex environments, highlighting current limitations and benefits of multi-sensory fusion.

Manolis Savva, Angel X. Chang, Alexey Dosovitskiy et al.

Proposes structured subgroup fairness auditing and learning algorithms based on weak agnostic learning, addressing fairness gerrymandering.

Michael Kearns, Seth Neel, Aaron Roth et al.

CARLA platform enables comprehensive testing of modular, imitation, and reinforcement learning methods for urban autonomous driving, with success rates over 95% in training scenarios.

Alexey Dosovitskiy, German Ros, Felipe Codevilla et al.

VQ-VAE combines discrete latent variables with vector quantization, achieving near state-of-the-art likelihoods and enabling high-quality multimodal generation.

Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu

Hierarchical genetic encoding plus evolution reaches 3.63% CIFAR-10 error and 20.3% top-1 on ImageNet.

Hanxiao Liu, Karen Simonyan, Oriol Vinyals et al.

Proposes MLDG, a model-agnostic meta-learning method, achieving state-of-the-art domain generalization results on image classification and reinforcement tasks.

Da Li, Yongxin Yang, Yi-Zhe Song et al.