FlowNet learns dense optical flow end-to-end, training on Flying Chairs and reaching 5–10 fps while generalizing to Sintel and KITTI.

Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg et al.

Introduced HED, a new edge detection algorithm achieving an ODS F-score of 0.782 on the BSD500 dataset.

Saining Xie, Zhuowen Tu

MAP-Elites maps high-performance solutions across feature space, revealing solution distribution and diversity.

Jean-Baptiste Mouret, Jeff Clune

Proposes incremental sparse Gaussian process regression for continuous-time trajectory estimation, achieving 3x speedup while maintaining accuracy.

Xinyan Yan, Vadim Indelman, Byron Boots

Kernel Manifold Alignment (KEMA) enables multi-source, unpaired domain alignment with superior performance on synthetic and real datasets.

Devis Tuia, Gustau Camps-Valls

Proposes end-to-end training of deep visuomotor policies using Guided Policy Search with a 92,000-parameter CNN for direct image-to-torque mapping.

Sergey Levine, Chelsea Finn, Trevor Darrell et al.

K-FAC approximates Fisher matrix via Kronecker decomposition, enabling faster natural gradient optimization in neural networks.

James Martens, Roger Grosse

LINE efficiently embeds large-scale networks by optimizing first- and second-order proximities with edge sampling, handling millions of nodes and billions of edges.

Jian Tang, Meng Qu, Mingzhe Wang et al.

DC-IGN learns interpretable image representations using SGVB, generating images with varied poses and lighting.

Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli et al.

Knowledge distillation transfers ensemble model knowledge into a single small model, achieving near-ensemble performance on MNIST and speech recognition tasks.

Geoffrey Hinton, Oriol Vinyals, Jeff Dean

The Bayesian Case Model (BCM) integrates case-based reasoning with a generative framework.

Been Kim, Cynthia Rudin, Julie Shah

Support function-based online convex optimization achieves Blackwell approachability with O(T−1/2) convergence.

Nahum Shimkin

Exp3.G classifies feedback graphs: strongly observable gives ~√(αT), weakly observable ~δ^(1/3)T^(2/3), and unobservable Θ(T).

Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel et al.

Proposed Deep Convolutional Neural Field model outperforms existing methods in monocular depth estimation.

Fayao Liu, Chunhua Shen, Guosheng Lin et al.

Proposes a meta-algorithm (SAOL) transforming low-regret algorithms into strongly adaptive ones, ensuring near-optimal performance on every interval with \( O(\log T) \) overhead.

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

Proposes 20 synthetic QA tasks to evaluate reasoning; extends Memory Networks, revealing current model limitations in multi-step inference.

Jason Weston, Antoine Bordes, Sumit Chopra et al.

TRPO (Trust Region Policy Optimization) guarantees monotonic policy improvement using KL constraints, excelling in large neural network policy training for robotics and Atari games.

John Schulman, Sergey Levine, Philipp Moritz et al.

PESC outperforms EI methods in Bayesian optimization with unknown constraints.

José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman et al.

Proposes a multi-layer LSTM encoder-decoder framework for unsupervised video representation learning, improving action recognition especially with limited labeled data.

Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov

SLIM method optimizes medical scoring systems' accuracy and sparsity via integer programming.

Berk Ustun, Cynthia Rudin