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Found 16 papers, 7 papers with code
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.
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.
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance.
MP-Former: Mask-Piloted Transformer for Image Segmentation
We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation.
Closed-Loop Transcription via Convolutional Sparse Coding
Our method is arguably the first to demonstrate that a concatenation of multiple convolution sparse coding/decoding layers leads to an interpretable and effective autoencoder for modeling the distribution of large-scale natural image datasets.
Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance.
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.
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
In this paper we present Mask DINO, a unified object detection and segmentation framework.
Ranked #1 on
Panoptic Segmentation
on COCO test-dev
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.
Ranked #1 on
Real-Time Object Detection
on COCO 2017 val
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.
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small.
General Convolutional Sparse Coding with Unknown Noise
However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic.
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing.
Scalable Online Convolutional Sparse Coding
Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data.
