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 Spoken Language Biomarkers for Detecting Cognitive Impairment

   

In this study we developed an automated system that evaluates speech and language features from audio recordings of neuropsychological examinations of 92 subjects in the Framingham Heart Study. A total of 265 features were used in an elastic-net regularized binomial logistic regression model to cla…


 Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition

 

Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling…


 Recent Advances in Convolutional Neural Networks

    

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging…


 ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network

 

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function predic…


 Learning Differentially Private Language Models Without Losing Accuracy

   

We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees without sacrificing predictive accuracy. Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gra…


 The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications

 

The principle goal of computational mechanics is to define pattern and structure so that the organization of complex systems can be detected and quantified. Computational mechanics developed from efforts in the 1970s and early 1980s to identify strange attractors as the mechanism driving weak fluid…


 Learning weakly supervised multimodal phoneme embeddings

  

Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e. audio and visual information representing the lips movements, …


 A Survey Of Cross-lingual Word Embedding Models

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-ling…


 Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

   

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present…


 Multiplicative LSTM for sequence modelling

  

We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions …


 Regina Barzilay wins MacArthur “genius grant”

MIT computer scientist who studies natural language processing and machine learning wins $625,000 prize. Regina Barzilay wins MacArthur “genius grant” by Adam Conner-Simons | CSAIL


 Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

Solving algebraic word problems requires executing a series of arithmetic operations—a program—to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, …


 Geo-referencing Place from Everyday Natural Language Descriptions

 

Natural language place descriptions in everyday communication provide a rich source of spatial knowledge about places. An important step to utilize such knowledge in information systems is geo-referencing all the places referred to in these descriptions. Current techniques for geo-referencing place…


 Interactive Learning of State Representation through Natural Language Instruction and Explanation

One significant simplification in most previous work on robot learning is the closed-world assumption where the robot is assumed to know ahead of time a complete set of predicates describing the state of the physical world. However, robots are not likely to have a complete model of the world especi…


 Can Machines Think in Radio Language?

People can think in auditory, visual and tactile forms of language, so can machines principally. But is it possible for them to think in radio language? The answer may give an exceptional solution for robot astronauts to talk with each other in space exploration. Can Machines Think in Radio Languag…


 Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling

 

Recurrent neural networks have shown remarkable success in modeling sequences. However low resource situations still adversely affect the generalizability of these models. We introduce a new family of models, called Lattice Recurrent Units (LRU), to address the challenge of learning deep multi-laye…


 Tableaux for Policy Synthesis for MDPs with PCTL* Constraints

Markov decision processes (MDPs) are the standard formalism for modelling sequential decision making in stochastic environments. Policy synthesis addresses the problem of how to control or limit the decisions an agent makes so that a given specification is met. In this paper we consider PCTL*, the …


 Learning Visual Reasoning Without Strong Priors

 

Achieving artificial visual reasoning – the ability to answer image-related questions which require a multi-step, high-level process – is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process…


 Modular Representation of Layered Neural Networks

   

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural ne…


 AutoMode: Relational Learning With Less Black Magic

Relational databases are valuable resources for learning novel and interesting relations and concepts. Relational learning algorithms learn the Datalog definition of new relations in terms of the existing relations in the database. In order to constraint the search through the large space of candid…


 GaDei: On Scale-up Training As A Service For Deep Learning

   

Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user’s dataset is prohi…


 How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

  

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has …


 DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks

       

Deep neural networks have become widely used, obtaining remarkable results in domains such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bio-informatics, where they have produced results comparable to human…


 Computer Assisted Composition with Recurrent Neural Networks

  

Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals, by combining with human input. To this end we generalise previous work, which sampled Markovian sequence models under the con…


 Language-depedent I-Vectors for LRE15

A standard recipe for spoken language recognition is to apply a Gaussian back-end to i-vectors. This ignores the uncertainty in the i-vector extraction, which could be important especially for short utterances. A recent paper by Cumani, Plchot and Fer proposes a solution to propagate that uncertain…