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Found 12 papers, 2 papers with code
Temporal Consistency for LLM Reasoning Process Error Identification
Verification is crucial for effective mathematical reasoning.
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
This issue is amplified when using original problems for in-context learning.
Stealthy Multi-Task Adversarial Attacks
This novel method demonstrates remarkable efficacy in compromising the target task while simultaneously maintaining or even enhancing performance across non-targeted tasks - a criterion hitherto unexplored in the field.
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
Inference-time alignment enhances the performance of large language models without requiring additional training or fine-tuning but presents challenges due to balancing computational efficiency with high-quality output.
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
Baidu runs the largest commercial web search engine in China, serving hundreds of millions of online users every day in response to a great variety of queries.
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions
In this paper, we propose a Domain Generalization based approach, named V2X-DGW, for LiDAR-based 3D object detection on multi-agent perception system under adverse weather conditions.
Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs
This study introduces bifurcated attention, a method designed to enhance language model inference in shared-context batch decoding scenarios.
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV.
Ranked #1 on
Oriented Object Detection
on EVD4UAV
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case.
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning.
Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning
Unsupervised learning is a challenging task due to the lack of labels.
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection.
