Search Results for author:
Found 55 papers, 20 papers with code
OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
In this paper, we propose a method for linking an open set of entities that does not require any span annotations.
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications.
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation
To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning.
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
Tables contain valuable knowledge in a structured form.
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models.
Now I Remember! Episodic Memory For Reinforcement Learning
Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car.
Avoiding Catastrophic States with Intrinsic Fear
Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never.
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.
A Neural-Symbolic Approach to Design of CAPTCHA
To address this, this paper promotes image/visual captioning based CAPTCHAs, which is robust against machine-learning-based attacks.
Tensor Product Generation Networks for Deep NLP Modeling
We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks.
StyleNet: Generating Attractive Visual Captions With Styles
We propose a novel framework named StyleNet to address the task of generating attractive captions for images and videos with different styles.
Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet).
Towards Neural Phrase-based Machine Translation
In this paper, we present Neural Phrase-based Machine Translation (NPMT).
Ranked #7 on
Machine Translation
on IWSLT2015 English-German
Question-Answering with Grammatically-Interpretable Representations
In our application of TPRN, internal representations learned by end-to-end optimization in a deep neural network performing a textual question-answering (QA) task can be interpreted using basic concepts from linguistic theory.
Scaffolding Networks: Incremental Learning and Teaching Through Questioning
We introduce a new paradigm of learning for reasoning, understanding, and prediction, as well as the scaffolding network to implement this paradigm.
Unsupervised Sequence Classification using Sequential Output Statistics
Although it is harder to optimize in its functional form, a stochastic primal-dual gradient method is developed to effectively solve the problem.
Sequence Modeling via Segmentations
The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.
Character-level Deep Conflation for Business Data Analytics
Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity.
End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.
Semantic Compositional Networks for Visual Captioning
The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag.
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
We introduce intrinsic fear (IF), a learned reward shaping that guards DRL agents against periodic catastrophes.
Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
Natural language understanding (NLU) is a core component of a spoken dialogue system.
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
In this paper, we address this limitation by replacing symbolic queries with an induced "soft" posterior distribution over the KB that indicates which entities the user is interested in.
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.
Bi-directional Attention with Agreement for Dependency Parsing
We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions.
Ranked #4 on
Chinese Dependency Parsing
on Chinese Pennbank
Unsupervised Learning of Predictors from Unpaired Input-Output Samples
In particular, we show that with regularization via a generative model, learning with the proposed unsupervised objective function converges to an optimal solution.
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.
Basic Reasoning with Tensor Product Representations
In this paper we present the initial development of a general theory for mapping inference in predicate logic to computation over Tensor Product Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)).
Reasoning in Vector Space: An Exploratory Study of Question Answering
Question answering tasks have shown remarkable progress with distributed vector representation.
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
Stacked Attention Networks for Image Question Answering
Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively.
Ranked #5 on
Visual Question Answering (VQA)
on VQA v1 test-std
Recurrent Reinforcement Learning: A Hybrid Approach
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states.
Distributed Compressive Sensing: A Deep Learning Approach
As the proposed method is a data driven method, it is only applicable when training data is available.
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.
Language Models for Image Captioning: The Quirks and What Works
Two recent approaches have achieved state-of-the-art results in image captioning.
Joint Learning of Distributed Representations for Images and Texts
The learned representations attempt to capture the combination of various visual concepts and cues.
A Deep Embedding Model for Co-occurrence Learning
Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items.
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
The results show that the proposed method in this paper significantly outperforms it for web document retrieval task.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
We consider learning representations of entities and relations in KBs using the neural-embedding approach.
Ranked #9 on
Link Property Prediction
on ogbl-biokg
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Ranked #1 on
Image Captioning
on COCO Captions test
Learning Multi-Relational Semantics Using Neural-Embedding Models
In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework.
Learning Semantic Representations for the Phrase Translation Model
The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation sys-tem, leading to a gain of 0. 7-1. 0 BLEU points.
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor.
Learning Input and Recurrent Weight Matrices in Echo State Networks
In this paper, we devise a special technique that take advantage of this linearity in the output units of an ESN, to learn the input and recurrent matrices.
Learning deep structured semantic models for web search using clickthrough data
The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data.
Learning with Recursive Perceptual Representations
The use of random projections is key to our method, as we show in the experiments section, in which we observe a consistent improvement over previous --often more complicated-- methods on several vision and speech benchmarks.
Ranked #219 on
Image Classification
on CIFAR-10
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input.
