⏩ SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

A benchmark for evaluating Multimodal LLMs using multiple-choice questions.



🖥 Github: https://github.com/ailab-cvc/seed-bench

📕 Paper: https://arxiv.org/abs/2307.16125v1

☑️ Dataset: https://paperswithcode.com/dataset/seed-bench

@Machine_learn

Book: Git Repository
Management in 30 Days
Authors: Sumit Jaiswal
ISBN: 978-93-55518-071
year: 2023
pages: 290
Tags:#GIT
@Machine_learn

Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

🖥 Github: https://github.com/osvai/ske2grid

📕 Paper: https://arxiv.org/pdf/2308.07571v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/ucf101

@Machin_learn

تخفيف ويژه دو پكيچ يادگيري عميق ٤٥ جلسه اي و ياديگيري عميق با ٣٦ پروژه عملي براي دوستاني كه نياز دارند.

@Raminmousa

Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

🖥 Github: https://github.com/llvy21/duic

📕 Paper: https://arxiv.org/pdf/2308.07733v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pixel-art

@Machine_learn

S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

🖥 Github: https://github.com/sheng-eatamath/s3a

📕 Paper: https://arxiv.org/pdf/2308.12960v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-100

@Machine_learn

این تخفیف تا اخر شب پا برجاست

🐕 Reprogramming Under Constraints

🖥 Github: https://github.com/landskape-ai/reprogram_lt

📕 Paper: https://arxiv.org/pdf/2308.14969v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

@Machine_learn

⭐️ScrollNet: Dynamic Weight Importance for Continual Learning

git clone https://github.com/FireFYF/ScrollNet.git
cd ScrollNet

🖥 Github: https://github.com/firefyf/scrollnet

📕 Paper: https://arxiv.org/abs/2308.16567v1

🔥 Dataset: https://paperswithcode.com/dataset/tiny-imagenet

@Machine_learn

⚡️ Improving Pixel-based MIM by Reducing Wasted Modeling Capability

A new method that explicitly utilizes low-level features from shallow layers to aid pixel reconstruction.



🖥 Github: https://github.com/open-mmlab/mmpretrain

📕 Paper: https://arxiv.org/abs/2308.00261v1

⭐️Project: mmpretrain.readthedocs.io/en/latest/

☑️ Dataset: https://paperswithcode.com/dataset/coco

@Machine_learn

با عرض سلام موضوعات پيشنهادي تز
برای دوستانی که نیاز دارن در ادامه اورده شده است.


master thesis

پيش بيني بار كوتاه مدت با استفاده از رويكردهاي يادگيري تركيبي

طبقه بندي رضايت مشتريان بانكي و موسسات اعتباري با استفاده از رويكردهاي بازگشتي

طبقه بندي اخبار جعل با استفاده از رويكرد تنسور سه بعدي و bert

پيشبيني قيمت سهام با استفاده از اطلاعات تويتر و ماركت

پيش بيني قيمت crypto با استفاده از اطلاعات hashrate

phd thesis

بهبود رویکردهای یادگیری عمیق بر روی اخبار جعل و شایعات

بهبود رویکرد های یادگیری عمیق ترکیبی جهت دستیابی به پورتوفولی بهینه

بهبود رویکردهای ترکیبی یادگیری عمیق برای طبقه بندی crypto با استفاده از اطلاعات hashrate

ارائه رویکردهای مبتنی بر وزن دهی غیر تصادفی در یادگیری عمیق

بهبود یادگیری انتقالی در سری زمانی

ارائه مدل های انتقالی برای طبقه بندی های سری زمانی

جهت مشاوره موضوعات می تونین با بنده در ارتباط باشین


@Raminmousa

imbalanced-DL: Deep Imbalanced Learning in Python

🖥 Github: https://github.com/ntucllab/imbalanced-dl

📕 Paper: https://arxiv.org/pdf/2308.15457v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

@Machine_learn

✅ LISA: Reasoning Segmentation via Large Language Model

New segmentation task -- reasoning segmentation. The task is designed to output a segmentation mask given a complex and implicit query text.


🖥 Github: https://github.com/dvlab-research/lisa

📕 Paper: https://arxiv.org/abs/2308.00692v2

☑️ Dataset: https://github.com/dvlab-research/lisa#dataset

@Machine_learn

🎲 Anti-Exploration by Random Network Distillation, Tinkoff Research, ICML 2023

We propose a new ensemble-free offline RL algorithm called SAC-RND. We evaluate our method on the D4RL (Fu et al., 2020) benchmark, and show that SAC-RND achieves performance comparable to ensemble-based methods while outperforming ensemble-free approaches.


🖥 Github: https://github.com/tinkoff-ai/sac-rnd

🤓 Paper: https://proceedings.mlr.press/v202/nikulin23a.html

@Machine_learn

Book: Machine Learning: The Basics
Authors: Alexander Jung
ISBN: -
year: 2023
pages: 287
Tags:#ML
@Machine_learn

🚀 AgentBench: Evaluating LLMs as Agents.

AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.


🖥 Github: https://github.com/thudm/agentbench

📕 Paper: https://arxiv.org/abs/2308.03688v1

☑️ Dataset: https://paperswithcode.com/dataset/alfworld

@Machine_learn

✅ SSLRec: A Self-Supervised Learning Library for Recommendation

SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders.


🖥 Github: https://github.com/hkuds/sslrec

📕 Paper: https://arxiv.org/abs/2308.05697v1

⛓ Models: https://github.com/HKUDS/SSLRec/blob/main/docs/Models.md

☑️ Datasets: https://github.com/HKUDS/SSLRec/blob/main/docs/Models.md

ai_machinelearning_big_data

LightTBNet

🖥 Github: https://github.com/dani-capellan/LightTBNet

📕 Paper: https://arxiv.org/pdf/2309.02140v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/montgomery-county-x-ray-set

@Machine_learn