Search Results for author:
Found 79 papers, 31 papers with code
Extracting Relations between Radiotherapy Treatment Details
We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records.
Defining and Learning Refined Temporal Relations in the Clinical Narrative
We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative.
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process.
HiCaM: A Hierarchical-Causal Modification Framework for Long-Form Text Modification
Large Language Models (LLMs) have achieved remarkable success in various domains.
Generalized Category Discovery in Event-Centric Contexts: Latent Pattern Mining with LLMs
Generalized Category Discovery (GCD) aims to classify both known and novel categories using partially labeled data that contains only known classes.
An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework
Existing LLM-enabled multi-agent frameworks are predominantly limited to digital or simulated environments and confined to narrowly focused knowledge domain, constraining their applicability to complex engineering tasks that require the design of physical embodiment, cross-disciplinary integration, and constraint-aware reasoning.
Efficient and Universal Neural-Network Decoder for Stabilizer-Based Quantum Error Correction
Quantum error correction is crucial for large-scale quantum computing, but the absence of efficient decoders for new codes like quantum low-density parity-check (QLDPC) codes has hindered progress.
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Boltzmann generators tackle this problem by pairing powerful normalizing flows with importance sampling to obtain statistically independent samples under the target distribution.
Implicit Neural Representations for Chemical Reaction Paths
We show that neural networks can be optimized to represent minimum energy paths as continuous functions, offering a flexible alternative to discrete path-search methods like Nudged Elastic Band (NEB).
DeepThink: Aligning Language Models with Domain-Specific User Intents
Supervised fine-tuning with synthesized instructions has been a common practice for adapting LLMs to domain-specific QA tasks.
Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models
We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data.
From Intention To Implementation: Automating Biomedical Research via LLMs
Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets.
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years.
DocReLM: Mastering Document Retrieval with Language Model
With over 200 million published academic documents and millions of new documents being written each year, academic researchers face the challenge of searching for information within this vast corpus.
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details.
Rho-1: Not All Tokens Are What You Need
Unlike traditional LMs that learn to predict every next token in a corpus, Rho-1 employs Selective Language Modeling (SLM), which selectively trains on useful tokens that aligned with the desired distribution.
Measuring Gender and Racial Biases in Large Language Models
As AI based decision making tools are increasingly employed across diverse domains, our findings underscore the necessity of understanding and addressing the potential unequal outcomes to ensure equitable outcomes across social groups.
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models
Subsequently, the retrieval model correlates new inputs with relevant guidelines, which guide LLMs in response generation to ensure safe and high-quality outputs, thereby aligning with human values.
LOCR: Location-Guided Transformer for Optical Character Recognition
Academic documents are packed with texts, equations, tables, and figures, requiring comprehensive understanding for accurate Optical Character Recognition (OCR).
Self-consistent Validation for Machine Learning Electronic Structure
Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems.
Revealing Decurve Flows for Generalized Graph Propagation
\textbf{Secondly}, we propose the {\em Continuous Unified Ricci Curvature} (\textbf{CURC}), an extension of celebrated {\em Ollivier-Ricci Curvature} for directed and weighted graphs.
Competition-Level Problems are Effective LLM Evaluators
Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem recently.
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training
(2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems.
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings.
Ranked #2 on
Visual Question Answering
on BenchLMM
(using extra training data)
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback
Interactive search can provide a better experience by incorporating interaction feedback from the users.
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.
Graph Inductive Biases in Transformers without Message Passing
Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.
Ranked #1 on
Node Classification
on CLUSTER
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models
By utilizing iterative bootstrapping, our approach enables LLMs to autonomously rectify errors, resulting in more precise and comprehensive reasoning chains.
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance.
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
This is the first use of sparse convolution for 2D masked modeling.
Ranked #1 on
Instance Segmentation
on COCO 2017 val
Algebraically Rigorous Quaternion Framework for the Neural Network Pose Estimation Problem
The 3D pose estimation problem -- aligning pairs of noisy 3D point clouds -- is a problem with a wide variety of real-world applications.
OSAN: A One-Stage Alignment Network To Unify Multimodal Alignment and Unsupervised Domain Adaptation
An intuitive way to handle these two problems is to fulfill these tasks in two separate stages: aligning modalities followed by domain adaptation, or vice versa.
Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization
To this end, we detach a sharpness term from the loss which reflects the impact of quantization noise.
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts.
Ranked #1 on
Conversational Question Answering
on ConvFinQA
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting
Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2. 5.
Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document
In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document.
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information.
PROD: Progressive Distillation for Dense Retrieval
It is common that a better teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student.
Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation
In this paper, we intend to search an optimal network structure that can run in real-time for this problem.
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases.
Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches
Contrastive-based self-supervised learning methods achieved great success in recent years.
Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles
Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles.
Contrastive Graph Multimodal Model for Text Classification in Videos
However, to our knowledge, there is no existing work focused on the second step of video text classification, which will limit the guidance to downstream tasks such as video indexing and browsing.
Visual Encoding and Debiasing for CTR Prediction
In this paper, we present a visual encoding framework for CTR prediction to overcome these problems.
UWC: Unit-wise Calibration Towards Rapid Network Compression
This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance.
A Continuous Mapping For Augmentation Design
Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models.
BN-NAS: Neural Architecture Search with Batch Normalization
We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS).
PSViT: Better Vision Transformer via Token Pooling and Attention Sharing
Then, a compact set of the possible combinations for different token pooling and attention sharing mechanisms are constructed.
GLiT: Neural Architecture Search for Global and Local Image Transformer
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition.
Ranked #548 on
Image Classification
on ImageNet
Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting
In this paper, we present the Block-wise Abstract Syntax Tree Splitting method (BASTS for short), which fully utilizes the rich tree-form syntax structure in ASTs, for improving code summarization.
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels
We leverage Graph Neural Network and multi-task learning to design M$^3$Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games.
Inception Convolution with Efficient Dilation Search
To develop a practical method for learning complex inception convolution based on the data, a simple but effective search algorithm, referred to as efficient dilation optimization (EDO), is developed.
DETR for Crowd Pedestrian Detection
Furthermore, the bipartite match of ED harms the training efficiency due to the large ground truth number in crowd scenes.
Evolving Search Space for Neural Architecture Search
The automation of neural architecture design has been a coveted alternative to human experts.
PV-NAS: Practical Neural Architecture Search for Video Recognition
Recently, deep learning has been utilized to solve video recognition problem due to its prominent representation ability.
Adaptive Gradient Method with Resilience and Momentum
Several variants of stochastic gradient descent (SGD) have been proposed to improve the learning effectiveness and efficiency when training deep neural networks, among which some recent influential attempts would like to adaptively control the parameter-wise learning rate (e. g., Adam and RMSProp).
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Therefore, we propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides.
Improving Auto-Augment via Augmentation-Wise Weight Sharing
On CIFAR-10, we achieve a top-1 error rate of 1. 24%, which is currently the best performing single model without extra training data.
Once Quantized for All: Progressively Searching for Quantized Compact Models
Automatic search of Quantized Neural Networks (QNN) has attracted a lot of attention.
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text.
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy
Specifically, the difficulties for architecture searching in such a complex space has been eliminated by the proposed stabilized share-parameter proxy, which employs Stochastic Gradient Langevin Dynamics to enable fast shared parameter sampling, so as to achieve stabilized measurement of architecture performance even in search space with complex topological structures.
Attacking Recommender Systems with Augmented User Profiles
Recommendation Systems (RS) have become an essential part of many online services.
Computation Reallocation for Object Detection
However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal.
FLEN: Leveraging Field for Scalable CTR Prediction
By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications.
Ranked #15 on
Click-Through Rate Prediction
on Avazu
Improving One-shot NAS by Suppressing the Posterior Fading
In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.
Online Hyper-parameter Learning for Auto-Augmentation Strategy
Data augmentation is critical to the success of modern deep learning techniques.
AM-LFS: AutoML for Loss Function Search
The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation.
Synaptic Strength For Convolutional Neural Network
Inspired by the relevant concept in neural science literature, we propose Synaptic Pruning: a data-driven method to prune connections between input and output feature maps with a newly proposed class of parameters called Synaptic Strength.
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing.
Temporal Annotation in the Clinical Domain
The corpus is available to the community and has been proposed for use in a SemEval 2015 task.
