Exploiting linear filter structures with low-rank SVD and clustering speeds up CNN evaluation 2-3×, with <1% accuracy loss.

Remi Denton, Wojciech Zaremba, Joan Bruna et al.

Proposed a distributed OPF algorithm using ADMM and SOCP relaxation, achieving 0.6s convergence on a 2,065-bus network.

Qiuyu Peng, Steven H. Low

DeepWalk learns latent vertex representations via truncated random walks, enhancing classification performance.

Bryan Perozzi, Rami Al-Rfou, Steven Skiena

Proposes multi-sentence, multi-level video description framework using semantic representations and visual recognition, improving coherence and detail control.

Anna Senina, Marcus Rohrbach, Wei Qiu et al.

Using pretrained OverFeat features with linear SVM achieves state-of-the-art results across multiple vision tasks without fine-tuning.

Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan et al.

Introduces multi-scale invertible orientation scores for vessel enhancement, significantly improving crossing and bifurcation detection in retinal images.

Julius Hannink, Remco Duits, Erik Bekkers

Design user-centric data obfuscation mechanisms optimizing utility while ensuring differential and distortion privacy.

Reza Shokri

Proposes zero-bias autoencoders with decoupled activation functions, enabling high-dimensional data representation without extra regularization, improving recognition accuracy.

Kishore Konda, Roland Memisevic, David Krueger

Nested Dropout enforces ordered representations, ensuring unit identifiability, equivalent to PCA, enabling fast large-scale retrieval and adaptive compression.

Oren Rippel, Michael A. Gelbart, Ryan P. Adams

This survey introduces a taxonomy for multi-objective sequential decision-making, classifying algorithms based on scenarios, scalarization functions, and policy types.

Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson et al.

NEXUS+ on Millennium/MS-II shows clusters and filaments dominate mass, with >100 h^-1Mpc straight filaments.

Marius Cautun, Rien van de Weygaert, Bernard J. T. Jones et al.

Applying MCTS and MCCFR to approximate Nash equilibria in Poker, combined with RNR for robust responses, achieves faster learning and higher exploitability.

Marc Ponsen, Steven de Jong, Marc Lanctot

Analyzes belief updating with sets of probabilities under minimax, emphasizing rectangularity for time consistency and calibration.

Peter D Grunwald, Joseph Y Halpern

Proposes a distributed control strategy combining droop control and economic dispatch to achieve frequency synchronization and cost optimization in microgrids.

Florian Dörfler, John Simpson-Porco, Francesco Bullo

Petuum employs error-tolerant bounded synchronization and dynamic scheduling to enable scalable distributed ML with billions of parameters.

Eric P. Xing, Qirong Ho, Wei Dai et al.

Volumetric spanners use at most 12d support points to enable low-variance exploration and efficient near-optimal BLO over general convex sets.

Elad Hazan, Zohar Karnin, Raghu Mehka

Dropout significantly reduces catastrophic forgetting in neural networks, with experimental data showing 15% better retention of old tasks and 20% improved adaptation to new tasks.

Ian J. Goodfellow, Mehdi Mirza, Da Xiao et al.

Proposes spectral and local clustering graph networks with input-size-independent parameters, effective on low-dimensional graphs.

Joan Bruna, Wojciech Zaremba, Arthur Szlam et al.

Introduces reparameterization and auto-encoding variational Bayes (AEVB) for efficient inference in continuous latent variable models.

Diederik P Kingma, Max Welling

Gradient-based visualization of CNN class models and saliency maps enhances interpretability and weakly supervised object localization.

Karen Simonyan, Andrea Vedaldi, Andrew Zisserman