Search Results for author:
Found 65 papers, 23 papers with code
Analytical Reasoning of Text
In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
CPT: Competence-progressive Training Strategy for Few-shot Node Classification
This could lead the meta-learner to face complex tasks too soon, hindering proper learning.
Cross Initialization for Personalized Text-to-Image Generation
Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability.
Improving Entropy-Based Test-Time Adaptation from a Clustering View
This new perspective allows us to explore how entropy minimization influences test-time adaptation.
LEGO-Prover: Neural Theorem Proving with Growing Libraries
Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47. 1% to 50. 4%.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models.
LongCoder: A Long-Range Pre-trained Language Model for Code Completion
In this paper, we introduce a new task for code completion that focuses on handling long code input and propose a sparse Transformer model, called LongCoder, to address this task.
Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning
The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design.
UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning
Recent advances in vision-language pre-training have enabled machines to perform better in multimodal object discrimination (e. g., image-text semantic alignment) and image synthesis (e. g., text-to-image generation).
Selective Pre-training for Private Fine-tuning
Besides performance improvements, our framework also shows that with careful pre-training and private fine-tuning, smaller models can match the performance of much larger models that do not have access to private data, highlighting the promise of private learning as a tool for model compression and efficiency.
From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base
The parser uses the KoPL to generate the transparent logical forms.
Deep Hashing With Minimal-Distance-Separated Hash Centers
This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the non-convex nature of the problem.
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers
The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: (1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks; (2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task.
Improving Task Generalization via Unified Schema Prompt
Task generalization has been a long standing challenge in Natural Language Processing (NLP).
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning.
ProQA: Structural Prompt-based Pre-training for Unified Question Answering
Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
The goal of anomaly detection is to identify anomalous samples from normal ones.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on
Question Answering
on OTT-QA
ViT2Hash: Unsupervised Information-Preserving Hashing
Specifically, from pixels to continuous features, we first propose a feature-preserving module, using the corrupted image as input to reconstruct the original feature from the pre-trained ViT model and the complete image, so that the feature extractor can focus on preserving the meaningful information of original data.
SHGNN: Structure-Aware Heterogeneous Graph Neural Network
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.
Availability Attacks Create Shortcuts
We are the first to unveil an important population property of the perturbations of these attacks: they are almost \textbf{linearly separable} when assigned with the target labels of the corresponding samples, which hence can work as \emph{shortcuts} for the learning objective.
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.
Learning mixture of neural temporal point processes for event sequence clustering
To fill the gap, we propose Mixture of Neural Temporal Point Processes (NTPP-MIX), a general framework that can utilize many existing NTPPs for event sequence clustering.
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.
Learning to Complete Code with Sketches
To evaluate models, we consider both ROUGE as well as a new metric RegexAcc that measures success of generating completions matching long outputs with as few holes as possible.
Large Scale Private Learning via Low-rank Reparametrization
We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
AR-LSAT: Investigating Analytical Reasoning of Text
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.
REST: Relational Event-driven Stock Trend Forecasting
To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.
Neural Deepfake Detection with Factual Structure of Text
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
GraphCodeBERT: Pre-training Code Representations with Data Flow
Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.
Ranked #3 on
Type prediction
on ManyTypes4TypeScript
How Does Data Augmentation Affect Privacy in Machine Learning?
Even further, we show that the proposed approach can achieve higher MI attack success rates on models trained with some data augmentation than the existing methods on models trained without data augmentation.
Low-Resource Generation of Multi-hop Reasoning Questions
Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs.
Ranked #1 on
Common Sense Reasoning
on Event2Mind test
(BLEU metric)
SEEK: Segmented Embedding of Knowledge Graphs
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.
Ranked #1 on
Link Prediction
on YAGO37
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture.
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
We study question answering over a dynamic textual environment.
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning
In this work, we use multiple knowledge sources as fuels for the model.
Controllable Face Aging
Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e. g., race).
Gradient Perturbation is Underrated for Differentially Private Convex Optimization
By using the \emph{expected curvature}, we show that gradient perturbation can achieve a significantly improved utility guarantee that can theoretically justify the advantage of gradient perturbation over other perturbation methods.
Simultaneous Region Localization and Hash Coding for Fine-grained Image Retrieval
Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation.
Modal-aware Features for Multimodal Hashing
The modal-aware operation consists of a kernel network and an attention network.
Reasoning Over Semantic-Level Graph for Fact Checking
We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.
Ranked #2 on
Fact Verification
on FEVER
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text
This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills.
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment.
Fashion Editing with Adversarial Parsing Learning
Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.
Feature Pyramid Hashing
In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval.
Deep Policy Hashing Network with Listwise Supervision
The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain a whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e. g., mean average precision (MAP), and 3) the database network is updated as the query network.
Towards Multi-pose Guided Virtual Try-on Network
Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses.
Ranked #1 on
Virtual Try-on
on Deep-Fashion
Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base
We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base.
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.
Improving Question Answering by Commonsense-Based Pre-Training
Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge.
Question Generation from SQL Queries Improves Neural Semantic Parsing
We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data.
Neural Math Word Problem Solver with Reinforcement Learning
Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
Using Intermediate Representations to Solve Math Word Problems
To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.
Learning Fine-Grained Expressions to Solve Math Word Problems
This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.
Deep Recurrent Regression for Facial Landmark Detection
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.
