Proposed a quadratic droop control method for microgrid voltage stabilization and reactive power optimization.

John W. Simpson-Porco, Florian Dorfler, Francesco Bullo

Proposes an unsupervised multimodal approach combining video and narration, achieving 85% accuracy in key task step detection on a new dataset.

Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal et al.

Proposes a deep neural framework combining unsupervised sentence embeddings and video-text models for aligning books and movies, achieving over 70% accuracy.

Yukun Zhu, Ryan Kiros, Richard Zemel et al.

Deep visualization tools reveal computations in intermediate layers of convolutional neural networks.

Jason Yosinski, Jeff Clune, Anh Nguyen et al.

Proposes a Wasserstein distance-based loss for multi-label learning, utilizing entropic regularization for efficient approximation, enhancing semantic smoothness.

Charlie Frogner, Chiyuan Zhang, Hossein Mobahi et al.

Proposes spectral networks with graph estimation for non-Euclidean data, matching or surpassing Dropout networks with fewer parameters.

Mikael Henaff, Joan Bruna, Yann LeCun

Proposes a graph-based visual relationship model using large-scale Amazon data for clothing and accessory recommendations.

Julian McAuley, Christopher Targett, Qinfeng Shi et al.

Proposes a unified randomized iterative framework for linear systems, encompassing known algorithms like Kaczmarz and coordinate descent, with exponential convergence guarantees.

Robert M. Gower, Peter Richtárik

Permutation search is effective, but PP-Index/MI-file are not always faster than direct search.

Bilegsaikhan Naidan, Leonid Boytsov, Eric Nyberg

Pointer Networks leverage attention as pointers, enabling variable-length input-output mapping, successfully applied to convex hull, Delaunay triangulation, and TSP.

Oriol Vinyals, Meire Fortunato, Navdeep Jaitly

Proposed a connection importance learning-based pruning method; reduced AlexNet parameters by 9× and VGG-16 by 13× without accuracy loss.

Song Han, Jeff Pool, John Tran et al.

Path-SGD optimizes deep neural networks using path normalization, improving performance on datasets like MNIST.

Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro

Compositional training for TransE and bilinear models cuts path-query error by up to 76.2% and improves KBC.

Kelvin Guu, John Miller, Percy Liang

Proposes a hybrid deep learning and rule-based approach for text-to-3D scene generation, achieving significant improvements in scene fidelity and diversity, with a new dataset and evaluation metrics.

Angel Chang, Will Monroe, Manolis Savva et al.

Unsupervised visual representation learning via context prediction, enhancing object discovery on Pascal VOC 2011 dataset.

Carl Doersch, Abhinav Gupta, Alexei A. Efros

Proposes CNN-based joint instance segmentation and depth ordering via multi-scale patch prediction and MRF fusion, achieving state-of-the-art on KITTI.

Ziyu Zhang, Alexander G. Schwing, Sanja Fidler et al.

Proposes a multi-region CNN with semantic segmentation-aware features and iterative bounding box refinement, achieving 78.2% mAP on VOC2007.

Spyros Gidaris, Nikos Komodakis

Neural-Image-QA combines CNN and LSTM, doubling previous accuracy to 17.49%, advancing multi-modal visual question answering.

Mateusz Malinowski, Marcus Rohrbach, Mario Fritz

Proposes a deep convolutional neural network-based direct perception model estimating 13 key affordance indicators for autonomous driving, trained on 12 hours of video game data.

Chenyi Chen, Ari Seff, Alain Kornhauser et al.

Fast R-CNN achieves 9× faster training, 146× faster detection, with 66% mAP on VOC2007, by sharing features and end-to-end multi-task learning.

Ross Girshick