Deep Learning
On Numerosity of Deep Neural Networks
Recently, a provocative claim was published that number sense spontaneously emerges in a deep neural network trained merely for visual object recognition.
Learning Representations for Time Series Clustering
Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell
Neural Information Processing Systems http://nips.cc/
Semi-supervised Learning with Ladder Networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by V alpola [1] which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification in addition to permutation-invariant MNIST classification with all labels.
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS Han Shi
However, it is computationally very costly. As a remedy, the one-shot approach has emerged as a popular technique for accelerating NAS using weight-sharing.
Learning to Perform Local Rewriting for Combinatorial Optimization
Neural Information Processing Systems http://nips.cc/
Efficient Exact Verification of Binarized Neural Networks
Concerned with the reliability of neural networks, researchers have developed verification techniques to prove their robustness.
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna
Neural Information Processing Systems http://nips.cc/