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Found 22 papers, 10 papers with code
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder
Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.
A Generative Framework for Simultaneous Machine Translation
We propose a generative framework for simultaneous machine translation.
Scene Text Recognition with Semantics
Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise.
Contrastive Video-Language Learning with Fine-grained Frame Sampling
However, even in paired video-text segments, only a subset of the frames are semantically relevant to the corresponding text, with the remainder representing noise; where the ratio of noisy frames is higher for longer videos.
Logically Consistent Adversarial Attacks for Soft Theorem Provers
We propose a novel, generative adversarial framework for probing and improving these models' reasoning capabilities.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Guiding Visual Question Generation
In traditional Visual Question Generation (VQG), most images have multiple concepts (e. g. objects and categories) for which a question could be generated, but models are trained to mimic an arbitrary choice of concept as given in their training data.
Cross-Modal Generative Augmentation for Visual Question Answering
Experiments on Visual Question Answering as downstream task demonstrate the effectiveness of the proposed generative model, which is able to improve strong UpDn-based models to achieve state-of-the-art performance.
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation quality and low latency; and (b) visual information to support this process by providing additional (visual) contextual information which may be available before the textual input is produced.
Latent Variable Models for Visual Question Answering
Current work on Visual Question Answering (VQA) explore deterministic approaches conditioned on various types of image and question features.
Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision
In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations.
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data.
Learning with Stochastic Guidance for Navigation
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of high variance when applied in complex real world environments.
Robotics
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent.
Neural Allocentric Intuitive Physics Prediction from Real Videos
In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.
Memory Architectures in Recurrent Neural Network Language Models
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models.
Discovering Discrete Latent Topics with Neural Variational Inference
Topic models have been widely explored as probabilistic generative models of documents.
Ranked #2 on
Topic Models
on 20NewsGroups
Latent Intention Dialogue Models
Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research.
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution.
Neural Variational Inference for Text Processing
We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
Ranked #1 on
Question Answering
on QASent
Bayesian Optimisation for Machine Translation
This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems.
