Search Results for author:
Found 25 papers, 9 papers with code
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models
Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains.
Constructing Data Transaction Chains Based on Opportunity Cost Exploration
However, the inherent replicability and privacy concerns of data make it challenging to directly apply traditional trading theories to data markets.
Make Continual Learning Stronger via C-Flat
A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases.
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations
While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is a crucial ability in human communication.
SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations
We have released our code and software at:~\url{https://github. com/AnonymousEACLDemo/SADAS}.
InstructVideo: Instructing Video Diffusion Models with Human Feedback
To tackle this problem, we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning.
RDBench: ML Benchmark for Relational Databases
Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications.
Molecular De Novo Design through Transformer-based Reinforcement Learning
In contrast to the traditional RNN-based models, our proposed method exhibits superior performance in generating compounds predicted to be active against various biological targets, capturing long-term dependencies in the molecular structure sequence.
CITING: Large Language Models Create Curriculum for Instruction Tuning
Specifically, we employ a teacher LLM to create a curriculum for instruction tuning of the student LLM, namely Curriculum Instruction TunING (CITING).
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data.
Ranked #1 on
Zero-Shot Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase due to the adoption of more sophisticated models and large-scale datasets.
Refined Response Distillation for Class-Incremental Player Detection
Furthermore, we present the NBA-IOD and Volleyball-IOD datasets as the benchmark and investigate the IOD tasks of the players systematically.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery
In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
Progressive Learning without Forgetting
Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks.
Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms
Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction.
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications.
Ranked #16 on
Human-Object Interaction Detection
on HICO-DET
Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation
In this paper, we propose a response-based incremental distillation method, dubbed Elastic Response Distillation (ERD), which focuses on elastically learning responses from the classification head and the regression head.
Response-based Distillation for Incremental Object Detection
However, fine-tuning directly on a well-trained detection model with only new data will leads to catastrophic forgetting.
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
For each problem, we comprehensively introduce the existing DRL solutions.
Few-Shot Conversational Dense Retrieval
In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products.
Reinforced Contact Tracing and Epidemic Intervention
In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention.
On codes in the projective linear group $PGL(2,q)$
In this paper, we resolve a conjecture of Green and Liebeck [Disc.
Combinatorics
