Search Results for author:
Found 38 papers, 15 papers with code
Regulatory DNA sequence Design with Reinforcement Learning
The fitness of CREs, measured by their ability to modulate gene expression, highly depends on the nucleotide sequences, especially specific motifs known as transcription factor binding sites (TFBSs).
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance
In this work, we present DualDiff, a dual-branch conditional diffusion model designed to enhance driving scene generation across multiple views and video sequences.
Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions
This survey addresses this gap by providing a comprehensive review of diffusion models for tabular data.
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model
To address these challenges, we propose HybriDNA, a decoder-only DNA language model that incorporates a hybrid Transformer-Mamba2 architecture, seamlessly integrating the strengths of attention mechanisms with selective state-space models.
NatureLM: Deciphering the Language of Nature for Scientific Discovery
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages.
Fact-Preserved Personalized News Headline Generation
In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency.
Interpretable Enzyme Function Prediction via Residue-Level Detection
It uses a set of learnable functional queries to adaptatively extract different local representations from the sequence of residue-level features for predicting different EC numbers.
World Models Increase Autonomy in Reinforcement Learning
However, the training process of RL is far from automatic, requiring extensive human effort to reset the agent and environments.
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments.
Improving Zero-shot LLM Re-Ranker with Risk Minimization
In the Retrieval-Augmented Generation (RAG) system, advanced Large Language Models (LLMs) have emerged as effective Query Likelihood Models (QLMs) in an unsupervised way, which re-rank documents based on the probability of generating the query given the content of a document.
Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint
Large language models internalize enormous parametric knowledge during pre-training.
Dual-Tap Optical-Digital Feedforward Equalization Enabling High-Speed Optical Transmission in IM/DD Systems
Intensity-modulation and direct-detection (IM/DD) transmission is widely adopted for high-speed optical transmission scenarios due to its cost-effectiveness and simplicity.
CaKDP: Category-aware Knowledge Distillation and Pruning Framework for Lightweight 3D Object Detection
To address these issues we propose a simple yet effective Category-aware Knowledge Distillation and Pruning (CaKDP) framework for compressing 3D detectors.
AppAgent: Multimodal Agents as Smartphone Users
Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks.
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
To solve this issue we introduce EduGym, a set of educational reinforcement learning environments and associated interactive notebooks tailored for education.
Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling
Firstly, they cannot directly generate coherent motions and require additional operations such as interpolation to process the generated actions.
Two-Memory Reinforcement Learning
While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of parametric neural networks.
Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation
Referring image segmentation segments an image from a language expression.
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation
In this paper, we present a clear ablation study of post-exploration in a general intrinsically motivated goal exploration process (IMGEP) framework, that the Go-Explore paper did not show.
Continuous Episodic Control
Therefore, this paper introduces Continuous Episodic Control (CEC), a novel non-parametric episodic memory algorithm for sequential decision making in problems with a continuous action space.
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor
Finally, a flexible Android malware detection model based on GANs with code tensor (MTFD-GANs) is proposed.
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
Ranked #12 on
Molecule Captioning
on ChEBI-20
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards.
On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR
Based on the above intuition, we first investigate types of end-to-end encoder-decoder based models in the single-input dual-output (SIDO) multi-task framework, after which a novel asynchronous decoding with fuzzy Pinyin sampling method is proposed according to the one-to-one correspondence characteristics between Pinyin and Character.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification
Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model.
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Hierarchical interaction network for video object segmentation from referring expressions
In this paper, we investigate the problem of video object segmentation from referring expressions (VOSRE).
Ranked #1 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Optical Flow Estimation
Referring Expression Segmentation
+3
Logic Traps in Evaluating Attribution Scores
Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.
Potential-based Reward Shaping in Sokoban
While previous work has investigated the use of expert knowledge to generate potential functions, in this work, we study whether we can use a search algorithm(A*) to automatically generate a potential function for reward shaping in Sokoban, a well-known planning task.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
Alignment Rationale for Natural Language Inference
Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors.
Transfer Learning and Curriculum Learning in Sokoban
In reinforcement learning, learning actions for a behavior policy that can be applied to new environments is still a challenge, especially for tasks that involve much planning.
Explore User Neighborhood for Real-time E-commerce Recommendation
On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information.
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue.
Distributed, Parallel, and Cluster Computing
Anchor Diffusion for Unsupervised Video Object Segmentation
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow.
Ranked #21 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
Hetero-Center Loss for Cross-Modality Person Re-Identification
With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible.
Cross-Modality Person Re-identification
Person Re-Identification
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.
Learn to Interpret Atari Agents
Deep reinforcement learning (DeepRL) agents surpass human-level performance in many tasks.
