Search Results for author:
Found 65 papers, 14 papers with code
Few-Shot Class-Incremental Learning for Named Entity Recognition
In this work, we study a more challenging but practical problem, i. e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones.
class-incremental learning
Few-Shot Class-Incremental Learning
+4
Improving the performance of optical inverse design of multilayer thin films using CNN-LSTM tandem neural networks
In this paper, we propose to use not only MLP, but also convolutional neural network (CNN) or long short-term memory (LSTM) algorithms in the configuration of the TNN.
Disambiguation in Conversational Question Answering in the Era of LLM: A Survey
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language.
WaterFlow: Learning Fast & Robust Watermarks using Stable Diffusion
In this work, we propose WaterFlow (WF), a fast and extremely robust approach for high fidelity visual watermarking based on a learned latent-dependent watermark.
Training Robust Graph Neural Networks by Modeling Noise Dependencies
In real-world applications, node features in graphs often contain noise from various sources, leading to significant performance degradation in GNNs.
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants
The Adobe Experience Platform AI Assistant is a conversational tool that enables organizations to interact seamlessly with proprietary enterprise data through a chatbot.
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
As the use of large language model (LLM) agents continues to grow, their safety vulnerabilities have become increasingly evident.
From Selection to Generation: A Survey of LLM-based Active Learning
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training.
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent
By explicitly disentangling the optimization of visual understanding from task-specific alignment, MDGD preserves pre-trained visual knowledge while enabling efficient task adaptation.
Interactive Visualization Recommendation with Hier-SUCB
For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting.
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SCICAP Challenge 2023
This paper presents an overview of the first SCICAP Challenge and details the performance of various models on its data, capturing a snapshot of the fields state.
Understanding How Paper Writers Use AI-Generated Captions in Figure Caption Writing
By analyzing video recordings of the writing process through interaction analysis, we observed that participants often began by copying and refining AI-generated captions.
Multi-LLM Collaborative Caption Generation in Scientific Documents
(Diverse Caption Generation) We then employ a strategy of fine-tuning/prompting multiple LLMs on the captioning task to generate candidate captions.
GUI Agents: A Survey
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction.
Personalized Multimodal Large Language Models: A Survey
We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly.
Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text
In this work, we research user preferences to see a chart, table, or text given a question asked by the user.
Personalization of Large Language Models: A Survey
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications.
Survey of User Interface Design and Interaction Techniques in Generative AI Applications
The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so.
A Survey of Small Language Models
We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques.
VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use
In this paper, we present VipAct, an agent framework that enhances VLMs by integrating multi-agent collaboration and vision expert models, enabling more precise visual understanding and comprehensive reasoning.
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval
Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search.
Federated Large Language Models: Current Progress and Future Directions
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications.
A Multi-LLM Debiasing Framework
Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities.
Visual Prompting in Multimodal Large Language Models: A Survey
This paper presents the first comprehensive survey on visual prompting methods in MLLMs, focusing on visual prompting, prompt generation, compositional reasoning, and prompt learning.
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages.
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data
This paper proposes a framework, Learning to Reduce, that fine-tunes a language model with On-Policy Learning to generate a reduced version of an input structured data.
Hallucination Diversity-Aware Active Learning for Text Summarization
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i. e., texts that are factually incorrect or unsupported.
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Crafting effective captions for figures is important.
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to finetuning and efficiently balances exploration and exploitation in model selection.
Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models
The model learns to reduce the input context using On-Policy Reinforcement Learning and aims to improve the reasoning performance of a fixed LLM.
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases.
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data.
Leveraging Graph Diffusion Models for Network Refinement Tasks
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
Bias and Fairness in Large Language Models: A Survey
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere.
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Captions are crucial for understanding scientific visualizations and documents.
Fairness-Aware Graph Neural Networks: A Survey
In this article, we examine and categorize fairness techniques for improving the fairness of GNNs.
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer segments.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Prior work often treated figure caption generation as a vision-to-language task.
Graph Learning with Localized Neighborhood Fairness
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions.
Implicit Session Contexts for Next-Item Recommendations
Session-based recommender systems capture the short-term interest of a user within a session.
Bundle MCR: Towards Conversational Bundle Recommendation
MCR, which uses a conversational paradigm to elicit user interests by asking user preferences on tags (e. g., categories or attributes) and handling user feedback across multiple rounds, is an emerging recommendation setting to acquire user feedback and narrow down the output space, but has not been explored in the context of bundle recommendation.
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
Automatic Forecasting via Meta-Learning
In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.
Influence-guided Data Augmentation for Neural Tensor Completion
In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods.
Edge: Enriching Knowledge Graph Embeddings with External Text
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods.
Personalized Visualization Recommendation
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback.
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
Heterogeneous Graphlets
Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets.
Graph Deep Factors for Forecasting
GraphDF is a hybrid forecasting framework that consists of a relational global and relational local model.
ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
Finally, we observed a strong preference by the human experts in our user study towards the visualizations recommended by our ML-based system as opposed to the rule-based system (5. 92 from a 7-point Likert scale compared to only 3. 45).
From Static to Dynamic Node Embeddings
While previous work on dynamic modeling and embedding has focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale (e. g., 1 month), we propose the notion of an $\epsilon$-graph time-series that uses a fixed number of edges for each graph, and show its superiority over the time-scale representation used in previous work.
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Unfortunately, recent work has sometimes confused the notion of structural roles and communities (based on proximity) leading to misleading or incorrect claims about the capabilities of network embedding methods.
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
In this work, we investigate higher-order network motifs and develop techniques based on the notion of closing higher-order motifs that move beyond closing simple triangles.
Figure Captioning with Reasoning and Sequence-Level Training
In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure.
Dynamic Node Embeddings from Edge Streams
In this work, we propose using the notion of temporal walks for learning dynamic embeddings from temporal networks.
Heterogeneous Network Motifs
To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks.
Latent Network Summarization: Bridging Network Embedding and Summarization
Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (i. e., #nodes and #edges), while retaining the ability to derive node representations on the fly.
Social and Information Networks
Higher-order Graph Convolutional Networks
Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.
Attention Models in Graphs: A Survey
However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining.
HONE: Higher-Order Network Embeddings
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs.
