Alien Registration- Larochelle, Paul H. (Jackman, Somerset County)

https://digitalmaine.com/alien_docs/7047/thumbnail.jp

Low-cost representation for restricted Boltzmann machines

This paper presents a method for extracting a low-cost representation from restricted Boltzmann machines. The new representation can be considered as a compression of the network, requiring much less storage capacity while reasonably preserving the network's performance at feature learning. We show that the compression can be done by converting the weight matrix of real numbers into a matrix of three values {-1, 0, 1} associated with a score vector of real numbers. This set of values is similar enough to Boolean values which help us further translate the representation into logical rules. In the experiments reported in this paper, we evaluate the performance of our compression method on image datasets, obtaining promising results. Experiments on the MNIST handwritten digit classification dataset, for example, have shown that a 95% saving in memory can be achieved with no significant drop in accuracy

Byzantine Stochastic Gradient Descent

This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the $m$ machines which allegedly compute stochastic gradients every iteration, an $\alpha$-fraction are Byzantine, and can behave arbitrarily and adversarially. Our main result is a variant of stochastic gradient descent (SGD) which finds $\varepsilon$-approximate minimizers of convex functions in $T = \tilde{O}\big( \frac{1}{\varepsilon^2 m} + \frac{\alpha^2}{\varepsilon^2} \big)$ iterations. In contrast, traditional mini-batch SGD needs $T = O\big( \frac{1}{\varepsilon^2 m} \big)$ iterations, but cannot tolerate Byzantine failures. Further, we provide a lower bound showing that, up to logarithmic factors, our algorithm is information-theoretically optimal both in terms of sampling complexity and time complexity

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a model's prediction and an average baseline to the different features used as input to the model. Being based on solid game-theoretic principles, Shapley values uniquely satisfy several desirable properties, which is why they are increasingly used to explain the predictions of possibly complex and highly non-linear machine learning models. Shapley values are well calibrated to a user's intuition when features are independent, but may lead to undesirable, counterintuitive explanations when the independence assumption is violated. In this paper, we propose a novel framework for computing Shapley values that generalizes recent work that aims to circumvent the independence assumption. By employing Pearl's do-calculus, we show how these 'causal' Shapley values can be derived for general causal graphs without sacrificing any of their desirable properties. Moreover, causal Shapley values enable us to separate the contribution of direct and indirect effects. We provide a practical implementation for computing causal Shapley values based on causal chain graphs when only partial information is available and illustrate their utility on a real-world example.Comment: Accepted at 34th Conference on Neural Information Processing Systems (NeurIPS 2020

Clear Experimental Signature of Charge-Orbital density wave in Nd1−x_{1-x}Ca1+x_{1+x}MnO4_{4}

Single Crystals of Nd$_{1-x}$Ca$_{1+x}$MnO$_{4}$ have been prepared by the travelling floating-zone method, and possible evidence of a charge -orbital density wave in this material presented earlier [PRB68,092405 (2003)] using High Resolution Electron Microscopy [HRTEM] and Electron Diffraction [ED]. In the current note we present direct evidence of charge-orbital ordering in this material using heat capacity measurements. Our heat capacity measurements indicate a clear transition consistent with prior observation. We find two main transitions, one at temperature $T_{_H}=310-314$ K, and other at $T_{_A}=143$ K. In addition, we may also conclude that there is a strong electron-phonon coupling in this material.Comment: 7 pages, 8 figure

Zero-Shot Hashing via Transferring Supervised Knowledge

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.Comment: 11 page

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence

Resolution of the clinical features of tyrosinemia following orthotopic liver transplantation for hepatoma

The clinical history before transplantation and subsequent clinical and biochemical course of 3 children and one adult with hereditary tyrosinemia treated by orthotopic hepatic transplantation is described. All four patients are now free of their previous dietary restrictions and appear to be cured of both their metabolic disease and their hepatic neoplasm. © 1986 Elsevier Science Publishers B.V. All rights reserved