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 DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain a…


 Leveraging Distributional Semantics for Multi-Label Learning

 

We present a novel and scalable label embedding framework for large-scale multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using Distributional Semantics). Our approach draws inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS…


 AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms

  

In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer uses genetic algorithms (GA) coupled with a tightly constrained programming la…


 Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification

      

Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppo…


 SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs

 

The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a surrogate for the concept. This fact makes of ma…


 A segmental framework for fully-unsupervised large-vocabulary speech recognition

    

Zero-resource speech technology is a growing research area that aims to develop methods for speech processing in the absence of transcriptions, lexicons, or language modelling text. Early term discovery systems focused on identifying isolated recurring patterns in a corpus, while more recent full-c…


 Learning Intrinsic Sparse Structures within Long Short-term Memory

  

Model compression is significant for wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and in business clusters requiring quick responses to large-scale service requests. In this work, we focus on reducing the sizes of basic structures (including in…


 An Automated Text Categorization Framework based on Hyperparameter Optimization

 

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any super…


 Abstractions for AI-Based User Interfaces and Systems

Novel user interfaces based on artificial intelligence, such as natural-language agents, present new categories of engineering challenges. These systems need to cope with uncertainty and ambiguity, interface with machine learning algorithms, and compose information from multiple users to make decis…


 Neural Models for Key Phrase Detection and Question Generation

We propose a two-stage neural model to tackle question generation from documents. Our model first estimates the probability that word sequences in a document compose “interesting” answers using a neural model trained on a question-answering corpus. We thus take a data-driven approach to…


 Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages

We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguist…


 Perspectives for Evaluating Conversational AI

Conversational AI systems are becoming famous in day to day lives. In this paper, we are trying to address the following key question: To identify whether design, as well as development efforts for search oriented conversational AI are successful or not.It is tricky to define ‘success’ …


 DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding

  

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used in NLP tasks to capture the long-term and local dependencies respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable computation, significantly less training time,…


 SAM: Semantic Attribute Modulation for Language Modeling and Style Variation

 

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attribut…


 Guiding Reinforcement Learning Exploration Using Natural Language

  

In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and st…


 Using NLU in Context for Question Answering: Improving on Facebook’s bAbI Tasks

  

For the next step in human to machine interaction, Artificial Intelligence (AI) should interact predominantly using natural language because, if it worked, it would be the fastest way to communicate. Facebook’s toy tasks (bAbI) provide a useful benchmark to compare implementations for convers…


 Using NLU in Context for Question Answering: Improving on Facebook’s bAbI Tasks

  

For the next step in human to machine interaction, Artificial Intelligence (AI) should interact predominantly using natural language because, if it worked, it would be the fastest way to communicate. Facebook’s toy tasks (bAbI) provide a useful benchmark to compare implementations for convers…


 The Google Brain Team’s Approach to Research

 

Posted by Jeff Dean, Google Senior Fellow About a year ago, the Google Brain team first shared our mission “Make machines intelligent. Improve people’s lives.” In that time, we’ve shared updates on our work to infuse machine learning across Google products that hundreds of millions of users…


 Biased Importance Sampling for Deep Neural Network Training

      

Importance sampling has been successfully used to accelerate stochastic optimization in many convex problems. However, the lack of an efficient way to calculate the importance still hinders its application to Deep Learning. In this paper, we show that the loss value can be used as an alternative im…


 Empower Sequence Labeling with Task-Aware Neural Language Model

Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, i…


 Shifting Mean Activation Towards Zero with Bipolar Activation Functions

 

We propose a simple extension to the ReLU-family of activation functions that allows them to shift the mean activation across a layer towards zero. Combined with proper weight initialization, this alleviates the need for normalization layers. We explore the training of deep vanilla recurrent neural…


 Affective Neural Response Generation

  

Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotio…


 Event Representations for Automated Story Generation with Deep Neural Nets

  

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that…


 A Planning Approach to Monitoring Behavior of Computer Programs

 

We describe a novel approach to monitoring high level behaviors using concepts from AI planning. Our goal is to understand what a program is doing based on its system call trace. This ability is particularly important for detecting malware. We approach this problem by building an abstract model of …


 Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms

We present a straightforward source-to-source transformation that introduces justifications for user-defined constraints into the CHR programming language. Then a scheme of two rules suffices to allow for logical retraction (deletion, removal) of constraints during computation. Without the need to …