Search Results for author:
Found 36 papers, 22 papers with code
Guided Attention Multimodal Multitask Financial Forecasting with Inter-Company Relationships and Global and Local News
Most works on financial forecasting use information directly associated with individual companies (e. g., stock prices, news on the company) to predict stock returns for trading.
OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation
The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food, while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task.
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation
Large language models (LLMs) have shown excellent performance on various NLP tasks.
All in an Aggregated Image for In-Image Learning
This paper introduces a new in-context learning (ICL) mechanism called In-Image Learning (I$^2$L) that combines demonstration examples, visual cues, and chain-of-thought reasoning into an aggregated image to enhance the capabilities of Large Multimodal Models (e. g., GPT-4V) in multimodal reasoning tasks.
Generative Semi-supervised Graph Anomaly Detection
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the unsupervised setting in most GAD studies with a fully unlabeled graph.
Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites
The fine-grained object attributes and behaviors non-existent in the image may still be generated but not measured by the current evaluation methods.
On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs
Two research questions are formulated to investigate the accuracy of LLMs in recalling information from pre-training knowledge graphs and their ability to infer knowledge graph relations from context.
LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay
To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game.
LLM4Vis: Explainable Visualization Recommendation using ChatGPT
To obtain demonstration examples with high-quality explanations, we propose a new explanation generation bootstrapping to iteratively refine generated explanations by considering the previous generation and template-based hint.
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
To address the calculation errors and improve the quality of generated reasoning steps, we extend PS prompting with more detailed instructions and derive PS+ prompting.
Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference without training examples.
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e. g., ChatDoctor) or instruction data (e. g., Alpaca).
Efficient Cross-Modal Video Retrieval with Meta-Optimized Frames
In turn, the frame-level optimization is through gradient descent using the meta loss of video retrieval model computed on the whole video.
Explanation Guided Contrastive Learning for Sequential Recommendation
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions.
Improving Compositional Generalization in Math Word Problem Solving
To improve the compositional generalization in MWP solving, we propose an iterative data augmentation method that includes diverse compositional variation into training data and could collaborate with MWP methods.
Attention-based Feature Aggregation
To implement this module, we define two variants of attention: self-attention on the summed-up feature map, and cross-attention between two feature maps before summed up.
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects.
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs.
Ranked #8 on
Math Word Problem Solving
on Math23K
A Large-Scale Benchmark for Food Image Segmentation
Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks -- the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e. g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images.
Ranked #3 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
ENCONTER: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer
Pretrained using large amount of data, autoregressive language models are able to generate high quality sequences.
DeepStyle: User Style Embedding for Authorship Attribution of Short Texts
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications.
Transforming Facial Weight of Real Images by Editing Latent Space of StyleGAN
We present an invert-and-edit framework to automatically transform facial weight of an input face image to look thinner or heavier by leveraging semantic facial attributes encoded in the latent space of Generative Adversarial Networks (GANs).
On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation
Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.
Graph-to-Tree Learning for Solving Math Word Problems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.
Ranked #10 on
Math Word Problem Solving
on Math23K
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism
Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc.
Ranked #7 on
Cross-Modal Retrieval
on Recipe1M
RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System
Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes.
JPLink: On Linking Jobs to Vocational Interest Types
To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions.
FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging
FoodAI has made food logging convenient, aiding smart consumption and a healthy lifestyle.
Characterizing and Predicting Repeat Food Consumption Behavior for Just-in-Time Interventions
Human beings are creatures of habit.
Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning
Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels.
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits
Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps.
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle.
Ranked #8 on
Cross-Modal Retrieval
on Recipe1M
JobComposer: Career Path Optimization via Multicriteria Utility Learning
Its key premise is that the observed career trajectories in OPNs may not necessarily be optimal, and can be improved by learning to maximize the sum of payoffs attainable by following a career path.
Collective Semi-Supervised Learning for User Profiling in Social Media
The abundance of user-generated data in social media has incentivized the development of methods to infer the latent attributes of users, which are crucially useful for personalization, advertising and recommendation.
