Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Gradient-based learning applied to document recognition

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

The Nature of Statistical Learning Theory

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

/pdf/generative-adversarial-nets-1ofhan3sbf.pdf

Generative Adversarial Nets

Scaling large margin classifiers for spoken language understanding

Kernel methods are widely used in statistical learning techniques. We recently introduced a general kernel framework based on weighted transducers or rational relations, rational kernels, to extend kernel methods to the analysis of variable-length sequences or more generally weighted automata. These kernels are efficient to compute and have been successfully used in applications such as spoken-dialog classification. Not all rational kernels are positive definite and symmetric (PDS) however, a sufficient property for guaranteeing the convergence of discriminant classification algorithms such as Support Vector Machines. We present several theoretical results related to PDS rational kernels. We show in particular that under some conditions these kernels are closed under sum, product, or Kleene-closure and give a general method for constructing a PDS rational kernel from an arbitrary transducer defined on some non-idempotent semirings. We also show that some commonly used string kernels or similarity measures such as the edit-distance, the convolution kernels of Haussler, and some string kernels used in the context of computational biology are specific instances of rational kernels. Our results include the proof that the edit-distance over a non-trivial alphabet is not negative definite, which, to the best of our knowledge, was never stated or proved before.

/pdf/positive-definite-rational-kernels-4sip64k23f.pdf

Positive Definite Rational Kernels

ATT more training data that includes 2004, 2005, and 2006 SBD data; no SVM boundary adjustment; training SVM with high generalization capability (e.g., a smaller value of C). As a pilot task, rushes summarization aims to show the main objects and events in the raw material with least redundancy while maximizing the usability. We proposed a multimodal rushes summarization method that relies on both face and speech information. Evaluation results show that the new SBD system is highly effective and the human centric rushes summarization approach is concise and easy to understand.

/pdf/at-t-research-at-trecvid-2007-3e82owduds.pdf

At&t research at trecvid 2007

The proposed shot boundary determination (SBD) algorithm contains a set of finite state machine (FSM) based detectors for pure cut, fast dissolve, fade in, fade out, dissolve, and wipe. Support vector machines (SVM) are applied to the cut and dissolve detectors to further boost performance. Our SBD system was highly effective when evaluated in TRECVID 2006 (TREC video retrieval evaluation) and its performance was ranked highest overall.

https://www.ee.columbia.edu/ln/dvmm/publications/07/ICME2007_ATT.pdf

A Fast, Comprehensive Shot Boundary Determination System

Bloggers, professional reviewers, and consumers continuously create opinion--rich web reviews about products and services, with the result that textual reviews are now abundant on the web and often convey a useful overall rating (number of stars). However, an overall rating cannot express the multiple or conflicting opinions that might be contained in the text, or explicitly rate the different aspects of the evaluated entity. This work addresses the task of automatically predicting ratings, for given aspects of a textual review, by assigning a numerical score to each evaluated aspect in the reviews. We handle this task as both a regression and a classification modeling problem and explore several combinations of syntactic and semantic features. Our results suggest that classification techniques perform better than ranking modeling when handling evaluative text.

/pdf/capturing-the-stars-predicting-ratings-for-service-and-2k9ldvaro6.pdf

Patrick Haffner

Papers

Scaling large margin classifiers for spoken language understanding

Positive Definite Rational Kernels

At&t research at trecvid 2007

A Fast, Comprehensive Shot Boundary Determination System

Capturing the Stars: Predicting Ratings for Service and Product Reviews