Search Results for author:
Found 18 papers, 6 papers with code
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors
To stress test these detectors, we introduce a proxy-attack strategy that effortlessly compromises LLMs, causing them to produce outputs that align with human-written text and mislead detection systems.
TraceNet: Segment one thing efficiently
To address this, we propose and formulate a one tap driven single instance segmentation task that segments a single instance selected by a user via a positive tap.
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
Recent advancements in solving large-scale traveling salesman problems (TSP) utilize the heatmap-guided Monte Carlo tree search (MCTS) paradigm, where machine learning (ML) models generate heatmaps, indicating the probability distribution of each edge being part of the optimal solution, to guide MCTS in solution finding.
TimeX++: Learning Time-Series Explanations with Information Bottleneck
The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues.
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently.
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition.
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network architecture makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and how input observations influence decisions.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Multi View Spatial-Temporal Model for Travel Time Estimation
Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics.
Spatial-temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction
In an intelligent transportation system, the key problem of traffic forecasting is how to extract periodic temporal dependencies and complex spatial correlations.
Progressive Self-Guided Loss for Salient Object Detection
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images.
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and varying sizes.
Correlation Propagation Networks for Scene Text Detection
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).
Learning Markov Clustering Networks for Scene Text Detection
MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph.
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9. 5x higher energy efficiency.
A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing
In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP).
