Introduces a graph-based diagram parsing and reasoning framework using DPG, achieving 85.3% structure accuracy and 78.6% question answering accuracy on AI2D dataset.

Aniruddha Kembhavi, Mike Salvato, Eric Kolve et al.

Provides a comprehensive mathematical framework for convolution, pooling, and transposed convolution size calculations, validated with experiments on CIFAR-10 and ImageNet.

Vincent Dumoulin, Francesco Visin

Proposes MF-GP-UCB, a multi-fidelity Gaussian process bandit algorithm that leverages low-cost approximations to improve black-box optimization efficiency.

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva et al.

Proposes an unsupervised CNN for single-view depth estimation using stereo geometry, achieving comparable results to supervised methods without labeled data.

Ravi Garg, Vijay Kumar BG, Gustavo Carneiro et al.

Introduces bidirectional LSTM-CRF and transition-based models for NER, achieving state-of-the-art results without language-specific resources.

Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian et al.

Introduces Molecular Graph Convolutions, leveraging atom and bond features, achieving superior drug activity prediction with AUC up to 0.85.

Steven Kearnes, Kevin McCloskey, Marc Berndl et al.

ADVI automates variational inference via automatic differentiation, supporting non-conjugate models for large-scale Bayesian analysis.

Alp Kucukelbir, Dustin Tran, Rajesh Ranganath et al.

Weight normalization reparameterizes weights as w = g * (v / ||v||), accelerating training and improving convergence.

Tim Salimans, Diederik P. Kingma

Introduces Visual Genome, a large-scale dataset with dense object, relation, attribute annotations, enabling structured scene understanding.

Ranjay Krishna, Yuke Zhu, Oliver Groth et al.

Abstractive text summarization using sequence-to-sequence RNNs achieves state-of-the-art performance on two datasets.

Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos et al.

Introduces causal inference-based bias correction for recommendation evaluation and learning, using propensity-weighted estimators and scalable matrix factorization.

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh et al.

Proposes Equilibrium Propagation, a biologically plausible energy-based learning algorithm that estimates gradients via local perturbations, enabling training of recurrent neural networks without explicit backpropagation.

Benjamin Scellier, Yoshua Bengio

LIME algorithm explains any classifier's predictions using locally interpretable models, enhancing model trustworthiness.

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin

Proposes HOAG, an approximate gradient-based hyperparameter optimization method, outperforming traditional approaches in logistic and kernel ridge regression.

Fabian Pedregosa

Numerical optimal control with direct multiple shooting enables versatile, real-time trajectory generation for complex quadrotor systems.

Mathieu Geisert, Nicolas Mansard

Introduces multiCluster and multiCCA methods for training multilingual embeddings across 59 languages, using dictionaries and monolingual data without parallel corpora.

Waleed Ammar, George Mulcaire, Yulia Tsvetkov et al.

Proposes asynchronous deep reinforcement learning (A3C) framework, outperforming DQN on Atari with half the training time on CPU.

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza et al.

Introduces KFC, a structured probabilistic approximation of the Fisher matrix for convolution layers, significantly accelerating training.

Roger Grosse, James Martens

Proposed an algorithm to resolve unidentifiability in inverse reinforcement learning by observing behaviors across multiple environments.

Kareem Amin, Satinder Singh

Distributed ADMM-based algorithm optimizes PV inverter power management, improving user satisfaction and thermal loss performance.

Mohammadhafez Bazrafshan, Nikolaos Gatsis