Search Results for author:
Found 19 papers, 8 papers with code
Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval
Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities.
Intention-aware Denoising Diffusion Model for Trajectory Prediction
To decrease the inference time, we reduce the variable dimensions in the intention-aware diffusion process and restrict the initial distribution of the action-aware diffusion process, which leads to fewer diffusion steps.
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.
$Se^2$: Sequential Example Selection for In-Context Learning
The remarkable capability of large language models (LLMs) for in-context learning (ICL) needs to be activated by demonstration examples.
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
To address this, we introduce XRL-Bench, a unified standardized benchmark tailored for the evaluation and comparison of XRL methods, encompassing three main modules: standard RL environments, explainers based on state importance, and standard evaluators.
TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision
Then, the subsequence features are extracted to determine the presence of collective anomalies.
Label-Free Multivariate Time Series Anomaly Detection
In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow.
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data
Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data.
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works.
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning
Specifically, we cast the problem of finding adversarial flows that will be misclassified as a sequence generation task, which we solve with Amoeba, a novel reinforcement learning algorithm that we design.
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.
SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i. e. neighboring nodes are dissimilar, due to their local and uniform aggregation.
Detecting Multivariate Time Series Anomalies with Zero Known Label
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.
LEBP -- Language Expectation & Binding Policy: A Two-Stream Framework for Embodied Vision-and-Language Interaction Task Learning Agents
People always desire an embodied agent that can perform a task by understanding language instruction.
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks
Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams.
Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search
The NAR explores the quality tiers of the search space globally and classifies each individual to the tier they belong to according to its global ranking.
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression
Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel.
Fairness-aware Outlier Ensemble
Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
