Incorporating EXAA day-ahead prices as external regressors improves short-term European electricity price forecasts by 15-20%.

Florian Ziel, Rick Steinert, Sven Husmann

SRCNN achieves image super-resolution, improving PSNR to 27.95 dB.

Chao Dong, Chen Change Loy, Kaiming He et al.

Introducing Quasi-Monte Carlo feature maps for shift-invariant kernels, reducing feature dimension and improving approximation accuracy.

Haim Avron, Vikas Sindhwani, Jiyan Yang et al.

Deep Fried ConvNets replace fully connected layers with adaptive Fastfood transforms, reducing parameters by over 90% without accuracy loss.

Zichao Yang, Marcin Moczulski, Misha Denil et al.

Adam combines first and second moment estimates for adaptive learning rates, accelerating large-scale stochastic optimization.

Diederik P. Kingma, Jimmy Ba

Elastic Averaging SGD (EASGD) leverages elastic force to enable efficient distributed deep learning, reducing communication overhead while enhancing exploration, leading to faster convergence and better accuracy.

Sixin Zhang, Anna Choromanska, Yann LeCun

Implicit regularization, not network size, controls capacity in deep learning.

Behnam Neyshabur, Ryota Tomioka, Nathan Srebro

By adding a cross-covariance penalty to autoencoders, hidden factors of variation in deep networks are discovered.

Brian Cheung, Jesse A. Livezey, Arjun K. Bansal et al.

MatConvNet is a MATLAB-based CNN toolbox supporting fast prototyping and GPU acceleration, enabling training on large datasets like ImageNet.

Andrea Vedaldi, Karel Lenc

This paper presents a high-level noise simulation model for CCD and CMOS sensors, validated for accuracy.

Mikhail Konnik, James Welsh

A spherical spin-glass mapping explains why SGD in large networks reaches low-energy, high-quality local minima.

Anna Choromanska, Mikael Henaff, Michael Mathieu et al.

Falling Rule Lists is a classification model using ordered if-then rules with monotonically decreasing success probability.

Fulton Wang, Cynthia Rudin

Hypercolumns combine multi-layer CNN features for pixel-level object segmentation, boosting SDS from 49.7 to 60.0 mean APr.

Bharath Hariharan, Pablo Arbeláez, Ross Girshick et al.

Proposes a deep neural image captioning model combining CNN and LSTM, achieving BLEU-4 of 27.7, outperforming previous methods.

Oriol Vinyals, Alexander Toshev, Samy Bengio et al.

Proposes LRCN, combining CNN and LSTM for video recognition and captioning, outperforming single models with end-to-end training.

Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach et al.

Proposes Fully Convolutional Networks (FCN) for pixel-level semantic segmentation, achieving 62.2% mean IU, surpassing previous SOTA with end-to-end training.

Jonathan Long, Evan Shelhamer, Trevor Darrell

Dictionary learning combined with `1-norm sparse coding improves image representation and recognition accuracy.

Julien Mairal, Francis Bach, Jean Ponce

Study on feature transferability in deep neural networks, revealing significant differences across layers.

Jason Yosinski, Jeff Clune, Yoshua Bengio et al.

A* sampling algorithm leverages Gumbel processes and A* search to efficiently draw exact samples from continuous distributions, outperforming adaptive rejection sampling.

Chris J. Maddison, Daniel Tarlow, Tom Minka

Neural Turing Machines extend neural networks with external memory, enabling inference of simple algorithms like copying and sorting.

Alex Graves, Greg Wayne, Ivo Danihelka