Search Results for author:
Found 90 papers, 36 papers with code
A Continuous Opinion Dynamic Model in Co-evolving Networks--A Novel Group Decision Approach
In contrast to the traditional consensus oriented group decision making (GDM) framework, this paper proposes a framework with the co-evolution of both opinions and relationship networks to improve the potential consensus level of a group and help the group reach a stable state.
Causal nearest neighbor rules for optimal treatment regimes
In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime.
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases
For word level task our best run achieved MAE 0. 545 (ranked 2nd), PCC 0. 892 (ranked 2nd) in valence prediction and MAE 0. 857 (ranked 1st), PCC 0. 678 (ranked 2nd) in arousal prediction.
Intelligent Trainer for Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical reinforcement learning (RL), by leveraging a learned model to generate synthesized data for policy training purpose.
Model Architecture Controls Gradient Descent Dynamics: A Combinatorial Path-Based Formula
However, our theoretical understanding of how model architecture affects performance or accuracy is limited.
A Survey of Predictive Maintenance: Systems, Purposes and Approaches
This paper highlights the importance of maintenance techniques in the coming industrial revolution, reviews the evolution of maintenance techniques, and presents a comprehensive literature review on the latest advancement of maintenance techniques, i. e., Predictive Maintenance (PdM), with emphasis on system architectures, optimization objectives, and optimization methods.
Searching for Stage-wise Neural Graphs In the Limit
Search space is a key consideration for neural architecture search.
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study
Automatic process discovery from textual process documentations is highly desirable to reduce time and cost of Business Process Management (BPM) implementation in organizations.
Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders
While we focus on interface layout prediction, our model can be generally applicable for other layout prediction problems that involve tree structures and 2-dimensional placements.
Kalibre: Knowledge-based Neural Surrogate Model Calibration for Data Center Digital Twins
However, in the context of CFD, each search step requires long-lasting CFD model's iterated solving, rendering these approaches impractical with increased model complexity.
Mapping Natural Language Instructions to Mobile UI Action Sequences
We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it.
An Efficient Smoothing Proximal Gradient Algorithm for Convex Clustering
Our Sproga is faster than ADMM- or AMA-based convex clustering algorithms by one to two orders of magnitude.
CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller.
Robotics
EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors
Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction.
Robotics
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems.
AI-lead Court Debate Case Investigation
The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial.
Cross Copy Network for Dialogue Generation
In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e. g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation.
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions
Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection.
Distances to molecular clouds in the second Galactic quadrant
We present distances to 76 medium-sized molecular clouds and an extra large-scale one in the second Galactic quadrant ($104. 75^\circ <l<150. 25^\circ $ and $|b|<5. 25^\circ$), 73 of which are accurately measured for the first time.
Astrophysics of Galaxies
Existence of constant mean curvature 2-spheres in Riemannian 3-spheres
We prove the existence of branched immersed constant mean curvature 2-spheres in an arbitrary Riemannian 3-sphere for almost every prescribed mean curvature, and moreover for all prescribed mean curvatures when the 3-sphere is positively curved.
Differential Geometry Analysis of PDEs
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODE
Interactions with either environments or expert policies during training are needed for most of the current imitation learning (IL) algorithms.
Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection
With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace.
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions.
Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios.
Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning
Modeling tap or click sequences of users on a mobile device can improve our understandings of interaction behavior and offers opportunities for UI optimization by recommending next element the user might want to click on.
Template-free Prompt Tuning for Few-shot NER
Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words.
VUT: Versatile UI Transformer for Multimodal Multi-Task User Interface Modeling
Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models
Specifically, for each task, a label word set is first constructed by selecting a high-frequency word for each class respectively, and then, task-specific vectors are inserted into the inputs and optimized to manipulate the model predictions towards the corresponding label words.
Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models
The design process of user interfaces (UIs) often begins with articulating high-level design goals.
VUT: Versatile UI Transformer for Multi-Modal Multi-Task User Interface Modeling
Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.
Carrier Phase Ranging for Indoor Positioning with 5G NR Signals
Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI), and is expected to play a significant role in the upcoming era of AI.
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i. e., video snippets) are supervised by classifying labeled bags (i. e., untrimmed videos).
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis
We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous work.
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation.
Searching for Optimal Subword Tokenization in Cross-domain NER
Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA).
Bootstrap Latent Representations for Multi-modal Recommendation
Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e. g., user-user or item-item relation graph) to augment the learned representations of users and/or items.
Layer-refined Graph Convolutional Networks for Recommendation
Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing.
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network
Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN).
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
In this work, we conduct an empirical study on the "Unlabeled Entity Problem" and find that it leads to severe confusion between "O" and entities, decreasing class discrimination of old classes and declining the model's ability to learn new classes.
Machine Learning for a Sustainable Energy Future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy.
Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model
We propose a simple and novel method for generating 3D human motion from complex natural language sentences, which describe different velocity, direction and composition of all kinds of actions.
Ranked #23 on
Motion Synthesis
on HumanML3D
Inductive Graph Transformer for Delivery Time Estimation
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences.
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
Based on this finding, we propose a simple yet effective model, dubbed as FREEDOM, that FREEzes the item-item graph and DenOises the user-item interaction graph simultaneously for Multimodal recommendation.
Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models
With the recent success of diffusion models for text-to-image generation, we propose a generative image compression method that demonstrates the potential of saving an image as a short text embedding which in turn can be used to generate high-fidelity images which is equivalent to the original one perceptually.
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation
Diffusion-based text-to-image generation models like GLIDE and DALLE-2 have gained wide success recently for their superior performance in turning complex text inputs into images of high quality and wide diversity.
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Central to applying ML to software artifacts (like source or executable code) is converting them into forms suitable for learning.
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation
On top of the finding, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation.
MMRec: Simplifying Multimodal Recommendation
This paper presents an open-source toolbox, MMRec for multimodal recommendation.
A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions
Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e. g., purchasing and clicking).
Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation
To address the issue, we propose a novel Dual Graph Multitask framework for imbalanced Delivery Time Estimation (DGM-DTE).
EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images
Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two.
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
Smooth and Stepwise Self-Distillation for Object Detection
Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training.
Raising The Limit Of Image Rescaling Using Auxiliary Encoding
While the random sampling of latent variable $z$ is useful in generating diverse photo-realistic images, it is not desirable for image rescaling when accurate restoration of the HR image is more important.
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack.
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning
To address this issue, we propose a novel pre-training framework, named Multimodal Sequence Mixup for Sequential Recommendation (MSM4SR), which leverages both users' sequential behaviors and items' multimodal content (\ie text and images) for effectively recommendation.
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis
This work analyzes how the backward error affects the diffusion ODEs and the sample quality in DDPMs.
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
We demonstrate that the most commonly used fine-tuning technique from prior work is not robust enough to handle the dynamic nature of APIs, leading to the loss of previously acquired knowledge i. e., catastrophic forgetting.
GBSD: Generative Bokeh with Stage Diffusion
The bokeh effect is an artistic technique that blurs out-of-focus areas in a photograph and has gained interest due to recent developments in text-to-image synthesis and the ubiquity of smart-phone cameras and photo-sharing apps.
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications.
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes.
Better Zero-Shot Reasoning with Role-Play Prompting
This highlights its potential to augment the reasoning capabilities of LLMs.
Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
To our knowledge, this is the first work to explicitly model item popularity in recommendation systems.
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
In this paper, we deliver a comprehensive study of PEFT techniques for LLMs under the automated code generation scenario.
ABS-SGD: A Delayed Synchronous Stochastic Gradient Descent Algorithm with Adaptive Batch Size for Heterogeneous GPU Clusters
In this paper, we propose a delayed synchronous SGD algorithm with adaptive batch size (ABS-SGD) for heterogeneous GPU clusters.
Diffusion-based 3D Object Detection with Random Boxes
3D object detection is an essential task for achieving autonomous driving.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
SOFARI: High-Dimensional Manifold-Based Inference
In this paper, we suggest a novel approach called high-dimensional manifold-based SOFAR inference (SOFARI), drawing on the Neyman near-orthogonality inference while incorporating the Stiefel manifold structure imposed by the SVD constraints.
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images
MIB supplements the fixed sample grid in CNN of the conventional backbone network with tokens of different interaction ranges.
Rethinking Negative Pairs in Code Search
In our proposed loss function, we apply three methods to estimate the weights of negative pairs and show that the vanilla InfoNCE loss is a special case of Soft-InfoNCE.
Making Harmful Behaviors Unlearnable for Large Language Models
Specifically, we introduce ``security vectors'', a few new parameters that can be separated from the LLM, to ensure LLM's responses are consistent with the harmful behavior.
Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval
To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval.
AviationGPT: A Large Language Model for the Aviation Domain
The advent of ChatGPT and GPT-4 has captivated the world with large language models (LLMs), demonstrating exceptional performance in question-answering, summarization, and content generation.
Hypergraph Node Representation Learning with One-Stage Message Passing
HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian.
Retrieving Conditions from Reference Images for Diffusion Models
Newly developed diffusion-based techniques have showcased phenomenal abilities in producing a wide range of high-quality images, sparking considerable interest in various applications.
Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search
In code search, the Generation-Augmented Retrieval (GAR) framework, which generates exemplar code snippets to augment queries, has emerged as a promising strategy to address the principal challenge of modality misalignment between code snippets and natural language queries, particularly with the demonstrated code generation capabilities of Large Language Models (LLMs).
OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation
Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations.
Are Large Language Models Good Prompt Optimizers?
Despite the success, the underlying mechanism of this approach remains unexplored, and the true effectiveness of LLMs as Prompt Optimizers requires further validation.
PointMamba: A Simple State Space Model for Point Cloud Analysis
Recently, state space models (SSM), a new family of deep sequence models, have presented great potential for sequence modeling in NLP tasks.
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences due to out-of-distribution (OOD) issues, we allow each head to process in-distribution length by selecting and attending to important context chunks.
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation
Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance.
Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with Interpretability
While the incorporation of multimodal information could enhance the interpretability of these systems, current multimodal models represent users and items utilizing entangled numerical vectors, rendering them arduous to interpret.
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
To achieve this goal, we freeze the parameters of the default pre-trained models and then propose the Dynamic Adapter, which generates a dynamic scale for each token, considering the token significance to the downstream task.
Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction
In addition, our experimental results confirm that the simple model and complex model are complementary to each other.
SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i. e. a minimap).
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks
Specifically, we re-formulate both token and sentence classification tasks into a unified language modeling task, and map label spaces of different tasks into the same vocabulary space.
LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases
Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.
