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Apr 8, 20160 likes317 views

Maruyama Tetsutaro

白巧件玄毛 Noto sans CJK JP 卞仄凶日 slideshare 匹手白巧件玄互秏尹卅中及匹反卅中井

1. The document discusses fairness constraints in contextual bandit problems and classic bandit problems. 2. It shows that for classic bandits, 成(k^3) rounds are necessary and sufficient to achieve a non-trivial regret under fairness constraints. 3. For contextual bandits, it establishes a tight relationship between achieving fairness and Knows What it Knows (KWIK) learning, where KWIK learnability implies the existence of fair learning algorithms.

Differential privacy without sensitivity [NIPS2016掂心頗揃蹋]Kentaro Minami

Interaction Networks for Learning about Objects, Relations and PhysicsKen Kuroki

Conditional Image Generation with PixelCNN Decoderssuga93

The document summarizes research on conditional image generation using PixelCNN decoders. It discusses how PixelCNNs sequentially predict pixel values rather than the whole image at once. Previous work used PixelRNNs, but these were slow to train. The proposed approach uses a Gated PixelCNN that removes blind spots in the receptive field by combining horizontal and vertical feature maps. It also conditions PixelCNN layers on class labels or embeddings to generate conditional images. Experimental results show the Gated PixelCNN outperforms PixelCNN and achieves performance close to PixelRNN on CIFAR-10 and ImageNet, while training faster. It can also generate portraits conditioned on embeddings of people.

Introduction of "TrailBlazer" algorithmKatsuki Ohto

Fast and Probvably Seedings for k-MeansKimikazu Kato

The document proposes a new MCMC-based algorithm for initializing centroids in k-means clustering that does not assume a specific distribution of the input data, unlike previous work. It uses rejection sampling to emulate the distribution and select initial centroids that are widely scattered. The algorithm is proven mathematically to converge. Experimental results on synthetic and real-world datasets show it performs well with a good trade-off of accuracy and speed compared to existing techniques.

InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...Shuhei Yoshida

Improving Variational Inference with Inverse Autoregressive FlowTatsuya Shirakawa

Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...Kenko Nakamura

This document proposes a sketch-based box-covering algorithm to efficiently analyze the fractality of massive graphs. It summarizes that some real-world networks have been found to be fractal in nature, but existing algorithms for determining fractality are too slow for large networks. The proposed method uses min-hash to represent boxes implicitly and solves the box-covering problem efficiently in the sketch space using a binary search tree and heap, allowing fractality analysis of networks with millions of edges for the first time.

Learning to learn by gradient descent by gradient descentHiroyuki Fukuda

Value iteration networksFujimoto Keisuke

Value Iteration Networks is a machine learning method for robot path planning that can operate in new environments not seen during training. It works by predicting optimal actions through learning reward values for each state and propagating rewards to determine the sum of future rewards. The method was shown to be effective for planning in grid maps and continuous control tasks, and was even applied to navigation of Wikipedia links.

Development and Experiment of Deep Learning with Caffe and mafKenta Oono

Safe and Efficient Off-Policy Reinforcement Learningmooopan

This document summarizes the Retrace(竹) reinforcement learning algorithm presented by Remi Munos, Thomas Stepleton, Anna Harutyunyan and Marc G. Bellemare. Retrace(竹) is an off-policy multi-step reinforcement learning algorithm that is safe (converges for any policy), efficient (makes best use of samples when policies are close), and has lower variance than importance sampling. Empirical results on Atari 2600 games show Retrace(竹) outperforms one-step Q-learning and existing multi-step methods.

Dual Learning for Machine Translation (NIPS 2016)Toru Fujino

The paper introduces a dual learning algorithm that utilizes monolingual data to improve neural machine translation. The algorithm trains two translation models in both directions simultaneously. Experimental results show that when trained with only 10% of parallel data, the dual learning model achieves comparable results to baseline models trained on 100% of data. The dual learning mechanism also outperforms baselines when trained on full data and can help address the lack of large parallel corpora.

Matching networks for one shot learningKazuki Fujikawa

The document summarizes the paper "Matching Networks for One Shot Learning". It discusses one-shot learning, where a classifier can learn new concepts from only one or a few examples. It introduces matching networks, a new approach that trains an end-to-end nearest neighbor classifier for one-shot learning tasks. The matching networks architecture uses an attention mechanism to compare a test example to a small support set and achieve state-of-the-art one-shot accuracy on Omniglot and other datasets. The document provides background on one-shot learning challenges and related work on siamese networks, memory augmented neural networks, and attention mechanisms.