Search Results for author:
Found 46 papers, 24 papers with code
CodecLM: Aligning Language Models with Tailored Synthetic Data
To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.
ADAPT to Robustify Prompt Tuning Vision Transformers
The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks.
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).
PILOT: Legal Case Outcome Prediction with Case Law
We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
Ranked #3 on
Table-based Fact Verification
on TabFact
UniPredict: Large Language Models are Universal Tabular Classifiers
Specifically, we train a single LLM on an aggregation of 169 tabular datasets with diverse targets and compare its performance against baselines that are trained on each dataset separately.
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
CITING: Large Language Models Create Curriculum for Instruction Tuning
Specifically, we employ a teacher LLM to create a curriculum for instruction tuning of the student LLM, namely Curriculum Instruction TunING (CITING).
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks.
Matching Patients to Clinical Trials with Large Language Models
Given a patient note, TrialGPT predicts the patient's eligibility on a criterion-by-criterion basis and then consolidates these predictions to assess the patient's eligibility for the target trial.
PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications
The code is available at https://github. com/RyanWangZf/PyTrial.
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)
Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Tabular data prediction has been employed in medical applications such as patient health risk prediction.
AutoTrial: Prompting Language Models for Clinical Trial Design
Clinical trials are critical for drug development.
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
Rehearsal-based approaches are a mainstay of continual learning (CL).
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning
Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success.
Robust Meta Learning for Image based tasks
However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown.
Invariant Meta Learning for Out-of-Distribution Generalization
Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data.
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal.
QueryForm: A Simple Zero-shot Form Entity Query Framework
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning
Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications.
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Adversarial pruning compresses models while preserving robustness.
SparCL: Sparse Continual Learning on the Edge
SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.
Artificial Intelligence for In Silico Clinical Trials: A Review
In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.
Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials.
TransTab: Learning Transferable Tabular Transformers Across Tables
Can we leverage model pretraining on multiple distinct tables?
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting.
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.
Learning to Prompt for Continual Learning
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.
SurvTRACE: Transformers for Survival Analysis with Competing Events
In medicine, survival analysis studies the time duration to events of interest such as mortality.
PAC-Bayes Information Bottleneck
Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.
Deep Bayesian Unsupervised Lifelong Learning
We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.
Lifelong Learning based Disease Diagnosis on Clinical Notes
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.
Learn-Prune-Share for Lifelong Learning
In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.
Open-World Class Discovery with Kernel Networks
We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.
Instance-wise Feature Grouping
Moreover, the features that belong to each group, and the important feature groups may vary per sample.
Finding Influential Instances for Distantly Supervised Relation Extraction
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk.
Less Is Better: Unweighted Data Subsampling via Influence Function
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.
Streaming Adaptive Nonparametric Variational Autoencoder
We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.
Collaborative Deep Reinforcement Learning for Multi-Object Tracking
To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning.
