Papers Read on AI

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, SP (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link this https URL.

2020: Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Chris Wang

https://arxiv.org/pdf/2011.09607v2.pdf

We propose the first reference-based video superresolution (RefVSR) approach that utilizes reference videos for high-fidelity results. We focus on RefVSR in a triplecamera setting, where we aim at super-resolving a low resolution ultra-wide video utilizing wide-angle and telephoto videos. We introduce the first RefVSR network that recurrently aligns and propagates temporal reference features fused with features extracted from low-resolution frames.

2022: Junyong Lee, Myeong-Chun Lee, Sunghyun Cho, Seungyong Lee

Ranked #1 on Reference-based Video Super-Resolution on RealMCVSR Dataset

https://arxiv.org/pdf/2203.14537v1.pdf

We present a novel transformer-based architecture for global multi-object tracking. Our network takes a short sequence of frames as input and produces global trajectories for all objects. The core component is a global tracking transformer that operates on objects from all frames in the sequence. The transformer encodes object features from all frames, and uses trajectory queries to group them into trajectories. The trajectory queries are object features from a single frame and naturally produce unique trajectories.

2022: Xingyi Zhou, Tianwei Yin, V. Koltun, Phillip Krahenbuhl

Ranked #4 on Multi-Object Tracking on MOT17

https://arxiv.org/pdf/2203.13250v1.pdf

We present a novel transformer-based architecture for global multi-object tracking. Our network takes a short sequence of frames as input and produces global trajectories for all objects. The core component is a global tracking transformer that operates on objects from all frames in the sequence. The transformer encodes object features from all frames, and uses trajectory queries to group them into trajectories. The trajectory queries are object features from a single frame and naturally produce unique trajectories.

2022: Xingyi Zhou, Tianwei Yin, V. Koltun, Phillip Krahenbuhl

Ranked #4 on Multi-Object Tracking on MOT17

https://arxiv.org/pdf/2203.13250v1.pdf

Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight. Such a mechanism limits the learning capacity of detectors. In this paper, we explore a new weighting paradigm, termed dual weighting (DW), to specify pos and neg weights separately. We first identify the key influential factors of pos/neg weights by analyzing the evaluation metrics in object detection, and then design the pos and neg weighting functions based on them.

2022: Shuai Li, Chen-Hang He, Ruihuang Li, Lei Zhang

https://arxiv.org/pdf/2203.09730v1.pdf

SionnaTM is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. It enables the rapid prototyping of complex communication system architectures and provides native support for the integration of neural networks. Sionna implements a wide breadth of carefully tested state-of-the-art algorithms that can be used for benchmarking and end-to-end performance evaluation. This allows researchers to focus on their research, making it more impactful and reproducible, while saving time implementing components outside their area of expertise. This white paper provides a brief introduction to Sionna, explains its design principles and features, as well as future extensions, such as integrated ray tracing and custom CUDA kernels. We believe that Sionna is a valuable tool for research on next-generation communication systems, such as 6G, and we welcome contributions from our community.

2022: J. Hoydis, Sebastian Cammerer, Fayccal Ait Aoudia, Avinash Vem, Nikolaus Binder, Guillermo Marcus, Alexander Keller

https://arxiv.org/pdf/2203.11854v1.pdf

Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Ob-ject365) with carefully designed principles, and curate a larger dataset for improved detector pre-training.

2022: Likun Cai, Zhi-Li Zhang, Yi Zhu, Li Zhang, Mu Li, X. Xue

Ranked #1 on Object Detection on BigDetection val

https://arxiv.org/pdf/2203.13249v1.pdf

Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen [52], we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input.

2021: Constantin Eichenberg, Sid Black, Samuel Weinbach, Letitia Parcalabescu, A. Frank

https://arxiv.org/pdf/2112.05253v1.pdf

In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle.

2022: Dian Chen, Philipp Krahenbuhl

Ranked #1 on Autonomous Driving on CARLA Leaderboard

https://arxiv.org/pdf/2203.11934v1.pdf

Tracking often uses a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.

2022: Yutao Cui, Jiang Cheng, Limin Wang, Gangshan Wu

Ranked #1 on Visual Object Tracking on GOT-10k

https://arxiv.org/pdf/2203.11082v1.pdf