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Found 44 papers, 18 papers with code
ChartMoE: Mixture of Expert Connector for Advanced Chart Understanding
Combined with the vanilla connector, we initialize different experts in four distinct ways and adopt high-quality knowledge learning to further refine the MoE connector and LLM parameters.
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation
With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience.
Large Investment Model
Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.
MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
This noise hinders accurate retrieval and generation.
Think-on-Graph 2.0: Deep and Interpretable Large Language Model Reasoning with Knowledge Graph-guided Retrieval
Retrieval-augmented generation (RAG) has significantly advanced large language models (LLMs) by enabling dynamic information retrieval to mitigate knowledge gaps and hallucinations in generated content.
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model
Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry.
Context Graph
Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples.
DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction
In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation.
PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition
Base on these experiment results, the impact of non-IID finger vein data on performance of federated learning are analyzed, and the superiority of PAFedFV in accuracy and robustness are demonstrated.
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models
Subsequently, the retrieval model correlates new inputs with relevant guidelines, which guide LLMs in response generation to ensure safe and high-quality outputs, thereby aligning with human values.
Unlocking the Power of Large Language Models for Entity Alignment
To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy.
Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment
Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT.
QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model
Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence. However, tailoring these agents for specialized domains like quantitative investment remains a formidable task.
A Survey on Large Language Model Hallucination via a Creativity Perspective
Hallucinations in large language models (LLMs) are always seen as limitations.
ChartBench: A Benchmark for Complex Visual Reasoning in Charts
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in image understanding and generation.
A Principled Framework for Knowledge-enhanced Large Language Model
Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in critical scenarios.
Noisy Pair Corrector for Dense Retrieval
Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched.
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified knowledge.
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity.
Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors).
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies.
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance.
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models
By utilizing iterative bootstrapping, our approach enables LLMs to autonomously rectify errors, resulting in more precise and comprehensive reasoning chains.
Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios.
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts.
Ranked #1 on
Conversational Question Answering
on ConvFinQA
Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence
Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1. 0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2. 0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3. 0, applying deep learning techniques to discover complex nonlinear pricing rules.
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage.
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information.
Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping
We validate our insight on a range of RL tasks and show its improvement over baselines: (1) In offline RL, the conservative exploitation leads to improved performance based on off-the-shelf algorithms; (2) In online continuous control, multiple value functions with different shifting constants can be used to tackle the exploration-exploitation dilemma for better sample efficiency; (3) In discrete control tasks, a negative reward shifting yields an improvement over the curiosity-based exploration method.
Finger Multimodal Feature Fusion and Recognition Based on Channel Spatial Attention
In this paper, we propose a multimodal biometric fusion recognition algorithm based on fingerprints and finger veins (Fingerprint Finger Veins-Channel Spatial Attention Fusion Module, FPV-CSAFM).
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension.
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement.
Ranked #82 on
Object Detection
on COCO minival
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning.
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.
Reward Shifting for Optimistic Exploration and Conservative Exploitation
We verify our insight on a range of tasks: (1) In offline RL, the conservative exploitation leads to improved learning performance based on off-the-shelf algorithms; (2) In online continuous control, multiple value functions with different shifting constants can be used to trade-off between exploration and exploitation thus improving learning efficiency; (3) In online RL with discrete action space, a negative reward shifting brings an improvement over the previous curiosity-based exploration method.
VisMCA: A Visual Analytics System for Misclassification Correction and Analysis. VAST Challenge 2020, Mini-Challenge 2 Award: Honorable Mention for Detailed Analysis of Patterns of Misclassification
This paper presents VisMCA, an interactive visual analytics system that supports deepening understanding in ML results, augmenting users' capabilities in correcting misclassification, and providing an analysis of underlying patterns, in response to the VAST Challenge 2020 Mini-Challenge 2.
A model-based framework for learning transparent swarm behaviors
The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i. e., transitions in observations from the perspective of a single robot in the swarm) given a policy.
Novel Policy Seeking with Constrained Optimization
In problem-solving, we humans can come up with multiple novel solutions to the same problem.
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).
Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning
We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern.
Robotics
Deep CNN Ensemble with Data Augmentation for Object Detection
We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-the-art performance on the object detection task.
