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Found 31 papers, 15 papers with code
How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving
While intelligence of autonomous vehicles (AVs) has significantly advanced in recent years, accidents involving AVs suggest that these autonomous systems lack gracefulness in driving when interacting with human drivers.
Robotics Computer Science and Game Theory
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
This framework consists of three parts: 1) a local feature extraction module to learn representations for each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region-specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter-efficient compared to previous methods.
Particle-based Energetic Variational Inference
Using the EVI framework, we can derive many existing Particle-based Variational Inference (ParVI) methods, including the popular Stein Variational Gradient Descent (SVGD) approach.
GraphCrop: Subgraph Cropping for Graph Classification
We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification.
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
Global Existence of Classical Solutions for a Reactive Polymeric Fluid near Equilibrium
In this paper, we study a new micro-macro model for a reactive polymeric fluid, which is derived recently in [Y. Wang, T.-F. Zhang, and C. Liu, \emph{J. Non-Newton.
Analysis of PDEs 35A01, 35A15, 76A10, 76M30, 82D60
Mixup for Node and Graph Classification
In this work, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification.
Ranked #16 on
Node Classification
on Pubmed
Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not.
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
We propose a novel deterministic sampling method to approximate a target distribution $\rho^*$ by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy (MMD).
Adaptive Data Augmentation on Temporal Graphs
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
Structure-Aware Label Smoothing for Graph Neural Networks
Representing a label distribution as a one-hot vector is a common practice in training node classification models.
Time-Aware Neighbor Sampling for Temporal Graph Networks
In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time.
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN), to efficiently capture very long-range dependencies.
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.
GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction
GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.
Flashlight: Scalable Link Prediction with Effective Decoders
However, HadamardMLP lacks the scalability for retrieving top scoring neighbors on large graphs, since to the best of our knowledge, there does not exist an algorithm to retrieve the top scoring neighbors for HadamardMLP decoders in sublinear complexity.
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth.
How Fragile is Relation Extraction under Entity Replacements?
In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context.
A Causal View of Entity Bias in (Large) Language Models
Building upon this SCM, we propose causal intervention techniques to mitigate entity bias for both white-box and black-box settings.
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
To mitigate these limitations, we introduce the LargeST benchmark dataset.
Primacy Effect of ChatGPT
We have two main findings: i) ChatGPT's decision is sensitive to the order of labels in the prompt; ii) ChatGPT has a clearly higher chance to select the labels at earlier positions as the answer.
Speak Like a Native: Prompting Large Language Models in a Native Style
Specifically, with AlignedCoT, we observe an average +3. 2\% improvement for \texttt{gpt-3. 5-turbo} compared to the carefully handcrafted CoT on multi-step reasoning benchmarks. Furthermore, we use AlignedCoT to rewrite the CoT text style in the training set, which improves the performance of Retrieval Augmented Generation by 3. 6\%. The source code and dataset is available at https://github. com/yangzhch6/AlignedCoT
DeepEdit: Knowledge Editing as Decoding with Constraints
To enforce these constraints, we utilize a depth-first search to adaptively substitute new knowledge for the LLMs' original reasoning steps, greedily seeking the optimal path of multi-hop reasoning with new knowledge.
SLANG: New Concept Comprehension of Large Language Models
The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of large language models (LLMs).
LPNL: Scalable Link Prediction with Large Language Models
This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs.
Is Factuality Decoding a Free Lunch for LLMs? Evaluation on Knowledge Editing Benchmark
The rapid development of large language models (LLMs) enables them to convey factual knowledge in a more human-like fashion.
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
