Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent

• URL: http://arxiv.org/abs/2402.09844v3
• Date: Wed, 10 Jul 2024 15:56:14 GMT
• Title: Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent
• Authors: Quentin Gallouédec, Edward Beeching, Clément Romac, Emmanuel Dellandréa,
• Abstract summary: Jack of All Trades (JAT) is a transformer-based model with a unique design optimized for handling sequential decision-making tasks. JAT is the first model of its kind to be fully open-sourced at https://huggingface.co/jat-project/jat, including a pioneering general-purpose dataset.
• Score: 2.3967405016776384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The search for a general model that can operate seamlessly across multiple domains remains a key goal in machine learning research. The prevailing methodology in Reinforcement Learning (RL) typically limits models to a single task within a unimodal framework, a limitation that contrasts with the broader vision of a versatile, multi-domain model. In this paper, we present Jack of All Trades (JAT), a transformer-based model with a unique design optimized for handling sequential decision-making tasks and multi-modal data types. The JAT model demonstrates its robust capabilities and versatility by achieving strong performance on very different RL benchmarks, along with promising results on Computer Vision (CV) and Natural Language Processing (NLP) tasks, all using a single set of weights. The JAT model marks a significant step towards more general, cross-domain AI model design, and notably, it is the first model of its kind to be fully open-sourced at https://huggingface.co/jat-project/jat, including a pioneering general-purpose dataset.

Related papers

• SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation [61.392147185793476]
  We present a unified and versatile foundation model, namely, SEED-X. SEED-X is able to model multi-granularity visual semantics for comprehension and generation tasks. We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
  arXiv  Detail & Related papers  (2024-04-22T17:56:09Z)
• GiT: Towards Generalist Vision Transformer through Universal Language Interface [94.33443158125186]
  This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. GiT is a multi-task visual model, jointly trained across five representative benchmarks without task-specific fine-tuning.
  arXiv  Detail & Related papers  (2024-03-14T13:47:41Z)
• UNITS: A Unified Multi-Task Time Series Model [31.675845788410246]
  We introduce UniTS, a multi-task time series model that uses task tokenization to express predictive and generative tasks within a single model. Across 38 datasets spanning human activity sensors, healthcare, engineering, and finance domains, UniTS model performs favorably against 12 forecasting models, 20 classification models, 18 anomaly detection models, and 16 imputation models.
  arXiv  Detail & Related papers  (2024-02-29T21:25:58Z)
• UnIVAL: Unified Model for Image, Video, Audio and Language Tasks [105.77733287326308]
  UnIVAL model goes beyond two modalities and unifies text, images, video, and audio into a single model. Our model is efficiently pretrained on many tasks, based on task balancing and multimodal curriculum learning. Thanks to the unified model, we propose a novel study on multimodal model merging via weight generalization.
  arXiv  Detail & Related papers  (2023-07-30T09:48:36Z)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models [72.8156832931841]
  Generalist models are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model. We release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction.
  arXiv  Detail & Related papers  (2022-12-08T17:07:09Z)
• Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks [86.66733026149892]
  We propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-gnostic tasks. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks.
  arXiv  Detail & Related papers  (2022-11-17T18:59:52Z)
• HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning [14.412066456583917]
  We propose a transformer-based model for few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Our method is particularly effective for small target CNN architectures where learning a fixed universal task-independent embedding is not optimal. We extend our approach to a semi-supervised regime utilizing unlabeled samples in the support set and further improving few-shot performance.
  arXiv  Detail & Related papers  (2022-01-11T20:15:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.