Data Science by ODS.ai 🦜

2023-03-17 15:07:22 Software Vulnerability Prediction Knowledge Transferring Between Programming Languages

One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, authors address this issue by proposing a transfer learning technique to leverage available datasets and generate a model to detect common vulnerabilities in different programming languages. They use C source code samples to train a CNN model, then, they use Java source code samples to adopt and evaluate the learned model. The authors use code samples from two benchmark datasets: NIST Software Assurance Reference Dataset (SARD) and Draper VDISC dataset. The results show that proposed model detects vulnerabilities in both C and Java codes with average recall of 72%. Open / Comment

2023-03-14 02:06:38 OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception.

OpenOccupancy first surrounding semantic occupancy perception benchmar.

Github: https://github.com/jeffwang987/openoccupancy

Paper: https://arxiv.org/abs/2303.03991v1

Dataset: https://paperswithcode.com/dataset/synthcity

Project: https://www.mmlab-ntu.com/project/styleganex/

ai_machinelearning_big_data Open / Comment

2023-03-13 10:15:41 ​​Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

ChatGPT is a language interface with distinctive conversational competency and reasoning capabilities across many domains. However, it is currently unable to process or generate images from the visual world. To address this limitation, the authors propose a system called Visual ChatGPT that incorporates different Visual Foundation Models to enable users to interact with ChatGPT using both language and images. The system is capable of handling complex visual questions or instructions that require multiple AI models and steps. Additionally, it allows for feedback and corrections.

Rather than creating a new multimodal ChatGPT from scratch, the authors propose building Visual ChatGPT by incorporating various (22) Visual Foundation Models (VFMs) directly into ChatGPT. To facilitate the integration of these VFMs, the authors introduce a Prompt Manager that supports several functions. These include specifying the input-output formats of each VFM, converting visual information to language format, and managing the histories, priorities, and conflicts of different VFMs. With the Prompt Manager's help, ChatGPT can use these VFMs iteratively and receive their feedback until it satisfies the users' requirements or reaches the end condition.

Paper: https://arxiv.org/abs/2303.04671

Code link: https://github.com/microsoft/visual-chatgpt

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme

#deeplearning #nlp #transformer #sota #languagemodel #visual Open / Comment

2023-03-09 07:28:23 ​​PaLM-E: An Embodied Multimodal Language Model

In this paper, the authors introduce the concept of "embodied language models," which integrate real-world sensory information with language processing. This integration enables the models to perform tasks related to robotics and perception seamlessly.

To achieve this, the models are trained end-to-end using a large language model and multiple sensory inputs, including visual and textual information. These models can tackle complex tasks such as sequential robotic manipulation planning, visual question answering, and captioning. The results of evaluations demonstrate the effectiveness of this approach, including positive transfer across different domains.

The flagship model, PaLM-E-562B, is the crown jewel of this research. It excels in robotics tasks and delivers state-of-the-art performance on OK-VQA. Despite its specialization in robotics, this model maintains its generalist language capabilities.

Paper: https://arxiv.org/abs/2303.03378

Project link: https://palm-e.github.io/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme

#deeplearning #nlp #transformer #sota #languagemodel #robotics Open / Comment

2023-03-07 22:33:18 ChatML

OpenAI released ChatGPT API with Chat Markup Language. The basic idea behind ChatML is ensure the LLM model inputs are sent in structured format following ChatML and not as unstructured text.

https://github.com/openai/openai-python/blob/main/chatml.md Open / Comment

2023-03-06 12:29:42 ​​In-Context Instruction Learning

The authors introduce a novel approach called In-Context Instruction Learning (ICIL), which greatly enhances zero-shot task generalization performance for both pretrained and instruction-fine-tuned models. ICIL employs a single fixed prompt to evaluate all tasks, which is a concatenation of cross-task demonstrations. The authors demonstrate that even the most powerful instruction-fine-tuned baseline (text-davinci-003) benefits from ICIL by 9.3%, indicating that the effect of ICIL is complementary to instruction-based fine-tuning.

Paper: https://arxiv.org/abs/2302.14691

Code: https://github.com/seonghyeonye/ICIL

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-icil

#deeplearning #nlp #transformer #sota #languagemodel Open / Comment

2023-03-06 01:36:31 Hot news: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/

Training smaller foundation models like LLaMA is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making LLaMA available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a LLAMA model card that details how we built the model in keeping with our approach to Responsible AI practices.

In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla70B and PaLM-540B. We release all our models to the research community.

Model card: https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md

Paper: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/

Form to apply: https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform

Unfortunately, it's only for non-commercial purposes :(

"You will not, and will not permit, assist or cause any third party to:

a. use, modify, copy, reproduce, create derivative works of, or distribute the Software Products (or any derivative works thereof, works incorporating the Software Products, or any data produced by the Software), in whole or in part, for (i) any commercial or production purposes ... " Open / Comment

2023-02-26 17:18:12 ​​LLaMA: Open and Efficient Foundation Language Models

LLaMA is a set of large language models, ranging from 7B to 65B parameters, that have been trained on publicly available datasets containing trillions of tokens. The LLaMA-13B model performs better than GPT-3 (175B) on most benchmarks, and the LLaMA-65B model is competitive with other state-of-the-art models, such as Chinchilla70B and PaLM-540B. This suggests that it is possible to achieve excellent performance in language modeling without relying on proprietary or inaccessible datasets.

Paper: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/

Code: https://github.com/facebookresearch/llama

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-llama

#deeplearning #nlp #transformer #sota #languagemodel Open / Comment

2023-02-23 14:55:41 #cheatsheet #statistics Open / Comment

2023-02-20 15:41:47 ​​Scaling Vision Transformers to 22 Billion Parameters

Google Research authors present a recipe for training a highly efficient and stable Vision Transformer (ViT-22B) with 22B parameters, the largest dense ViT model to date. Experiments reveal that as the model's scale increases, its performance on downstream tasks improves. Additionally, ViT-22B shows an improved tradeoff between fairness and performance, state-of-the-art alignment with human visual perception in terms of shape/texture bias, and improved robustness. The authors suggest that ViT-22B demonstrates the potential for achieving “LLM-like” scaling in vision models and takes important steps toward that goal.

Paper: https://arxiv.org/abs/2302.05442

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-vit-22

#deeplearning #cv #transformer #sota Open / Comment