Search Results for author:
Found 35 papers, 24 papers with code
BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization
In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model.
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e. g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora.
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
We provide a first finite-particle convergence rate for Stein variational gradient descent (SVGD).
Neural Eigenfunctions Are Structured Representation Learners
In this paper, we introduce a scalable method for learning structured, adaptive-length deep representations.
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax.
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval
It also achieves about +4. 9 AR on COCO and +3. 8 AR on Flickr30K than LightingDot and achieves comparable performance with the state-of-the-art (SOTA) fusion-based model METER.
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems.
Gradient Estimation with Discrete Stein Operators
Gradient estimation -- approximating the gradient of an expectation with respect to the parameters of a distribution -- is central to the solution of many machine learning problems.
Schema-Free Dependency Parsing via Sequence Generation
To remedy these drawbacks, we propose to achieve universal and schema-free Dependency Parsing (DP) via Sequence Generation (SG) DPSG by utilizing only the pre-trained language model (PLM) without any auxiliary structures or parsing algorithms.
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
We introduce a variance reduction technique for score function estimators that makes use of double control variates.
Program Transfer for Answering Complex Questions over Knowledge Bases
In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations.
TWAG: A Topic-Guided Wikipedia Abstract Generator
Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques.
Sampling with Mirrored Stein Operators
We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries.
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer.
Neural Networks as Inter-Domain Inducing Points
Equivalences between infinite neural networks and Gaussian processes have been established for explaining the functional prior and training dynamics of deep learning models.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.
Nonparametric Score Estimators
Estimating the score, i. e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models that involve flexible yet intractable densities.
Unbiased Scene Graph Generation from Biased Training
Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".
Ranked #1 on
Scene Graph Generation
on Visual Genome
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities.
Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points, which can lead to more scalable algorithms than previous methods.
Scalable Training of Inference Networks for Gaussian-Process Models
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points.
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.
Functional Variational Bayesian Neural Networks
We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) defined directly on stochastic processes, i. e. distributions over functions.
Explainable and Explicit Visual Reasoning over Scene Graphs
We aim to dismantle the prevalent black-box neural architectures used in complex visual reasoning tasks, into the proposed eXplainable and eXplicit Neural Modules (XNMs), which advance beyond existing neural module networks towards using scene graphs --- objects as nodes and the pairwise relationships as edges --- for explainable and explicit reasoning with structured knowledge.
Ranked #10 on
Visual Question Answering
on CLEVR
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization.
Learning to Embed Sentences Using Attentive Recursive Trees
Sentence embedding is an effective feature representation for most deep learning-based NLP tasks.
Semi-crowdsourced Clustering with Deep Generative Models
We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.
A Spectral Approach to Gradient Estimation for Implicit Distributions
Recently there have been increasing interests in learning and inference with implicit distributions (i. e., distributions without tractable densities).
Message Passing Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
On Modeling Sense Relatedness in Multi-prototype Word Embedding
To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model.
ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
Kernel Implicit Variational Inference
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors.
Towards Better Analysis of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification.
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
We present a new perspective on neural knowledge base (KB) embeddings, from which we build a framework that can model symbolic knowledge in the KB together with its learning process.
