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snehananavati
Sneha Nanavati

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AIcrowd

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Challenges Entered

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failed 247893
graded 247892

Multi-Agent Dynamics & Mixed-Motive Cooperation

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Small Object Detection and Classification

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failed 235496

Understand semantic segmentation and monocular depth estimation from downward-facing drone images

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A benchmark for image-based food recognition

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Using AI For Building’s Energy Management

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What data should you label to get the most value for your money?

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Interactive embodied agents for Human-AI collaboration

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Behavioral Representation Learning from Animal Poses.

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Airborne Object Tracking Challenge

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ASCII-rendered single-player dungeon crawl game

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5 Puzzles 21 Days. Can you solve it all?

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Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

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5 Puzzles 21 Days. Can you solve it all?

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Self-driving RL on DeepRacer cars - From simulation to real world

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3D Seismic Image Interpretation by Machine Learning

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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Multi-Agent Reinforcement Learning on Trains

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A benchmark for image-based food recognition

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Sample-efficient reinforcement learning in Minecraft

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5 Puzzles, 3 Weeks. Can you solve them all? πŸ˜‰

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Multi-agent RL in game environment. Train your Derklings, creatures with a neural network brain, to fight for you!

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Predicting smell of molecular compounds

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5 Problems 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles, 3 Weeks | Can you solve them all?

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Grouping/Sorting players into their respective teams

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5 Problems 15 Days. Can you solve it all?

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5 Problems 15 Days. Can you solve it all?

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5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

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Remove Smoke from Image

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Classify Rotation of F1 Cars

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Can you classify Research Papers into different categories ?

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Can you dock a spacecraft to ISS ?

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Multi-Agent Reinforcement Learning on Trains

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Multi-Class Object Detection on Road Scene Images

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Localization, SLAM, Place Recognition, Visual Navigation, Loop Closure Detection

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Detect Mask From Faces

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Identify Words from silent video inputs.

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A Challenge on Continual Learning using Real-World Imagery

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graded 200977

Music source separation of an audio signal into separate tracks for vocals, bass, drums, and other

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failed 247893
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Improve RAG with Real-World Benchmarks

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Make Informed Decisions with Shopping Knowledge

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Amazon KDD Cup 24: All-Around

Amazon KDD Cup 2024: Multi-Task Online Shopping Ch

πŸ›οΈ Welcome to KDD Cup: 2024 Multi-Task Online Shopping Challenge for LLMS

Yesterday

Are you tired of the endless search for the perfect gift online? It’s like navigating a maze of products, reviews, and prices, only to feel overwhelmed by too many choices.

Introducing the Amazon Multi-Task Online Shopping Challenge, where we’re revolutionizing online shopping using Large Language Models (LLMs). Traditional methods miss the mark in understanding the nuance of shopping terminology, consumer behavior, and the wide array of products and languages, leaving users drowning in information.

Our ShopBench benchmark mirrors real-world shopping complexities, aiming to make online shopping as intuitive as having a knowledgeable assistant by your side. Participate to develop LLMs that can simplify shopping, making it a more intuitive and satisfying experience, much like a knowledgeable shopping assistant would in real life.

:shopping_cart: Multi-Task Online Shopping Challenge for LLMs

With 57 tasks and over 20,000 questions based on real Amazon data, this challenge pushes LLMs to excel in understanding shopping concepts, customer behavior, and multilingual support. Whether you’re a seasoned developer or a student, from the industry or academia, this challenge offers a platform to craft innovative LLM solutions that reshape online shopping experiences and valuable insights that benefit the whole community.

ShopBench, a comprehensive benchmark that mimics these real-world online shopping complexities, focuses on four main key shopping skills (which will serve as Tracks 1-4):

  • shopping concept understanding
  • shopping knowledge reasoning
  • user behavior alignment
  • multi-lingual abilities

Additionally, Track 5: All-around, promotes comprehensive solutions that address all tasks in Tracks 1-4 with a single, unified approach, offering larger rewards for these versatile solutions.

This challenge aims to give participants practical experience in crafting advanced LLM solutions for real issues, benefiting both the online service industry with robust, ready-to-implement LLM solutions and the wider machine learning community with valuable insights and training guidance.

:trophy: Exciting Prizes

The challenge offers a total prize pool of $41,500, divided into three categories:

  • Winner Prizes: Cash awards for the top three positions in each track.
  • AWS Credits: Awarded to teams ranking immediately after the top three in each track.
  • Student Awards: Special awards for the best student teams to support the development of resource-efficient LLM solutions due to the high computational costs and engineering efforts involved.

Prizes for Tracks 1-4:

  • :1st_place_medal: First place: $2,000
  • :2nd_place_medal: Second place: $1,000
  • :3rd_place_medal: Third place: $500
  • 4th-7th places receive AWS Credit of $500
  • :medal_sports: Student Award: $750

Prizes for Track 5 (All-around):

  • :1st_place_medal: First place: $7,000
  • :2nd_place_medal: Second place: $3,500
  • :3rd_place_medal: Third place: $1,500
  • 4th-8th places receive AWS Credit of $500
  • :medal_sports: Student Award: $2,000

Winners have the opportunity to present their work at the KDD Cup workshop 2024, held at ACM SIGKDD 2024 (August 2024, Barcelona, Spain).

:date: Challenge Timeline

  • Phase 1 Start Date: 21th March, 2024 23:55 UTC
  • Entry Freeze Deadline and Phase 1 End Date: 10th May, 2024 23:55 UTC
  • Phase 2 Start Date: 15th May, 2024 23:55 UTC
  • End Date: 10th July, 2024 23:55 UTC
  • Winner Notification: 15th July, 2024
  • Winner Announcement: 26th August, 2024 (At KDD 2024)

Signup now to begin this journey and dive into the challenge details. Join a community of innovative thinkers, share ideas, and engage in this exciting challenge.

:busts_in_silhouette: Challenges are more fun with teams. Find your teammate.
:speech_balloon: Have feedback or query? Share it with us.

All the best,
Team AIcrowd

πŸ‘₯ Looking for teammates?

14 days ago

Competing is more fun with a team!

Introduce yourself here, and find others who are looking to team up! :sparkles:

:writing_hand: Format:

  • A short introduction about you and your background.
  • What brings you to this challenge?
  • Some ideas you wish to explore as a part of this challenge?

All The Best!
Team Amazon KDD Cup

πŸ’¬ Feedback & Suggestions

14 days ago

We are constantly trying to improve this challenge for you and would appreciate any feedback you might have! :raised_hands:

Please reply to this thread with your suggestions and feedback on making the challenge better for you!

  • What have been your major pain points so far?
  • What would you like to see improved?

All the best,
Team Amazon KDD Cup

Meta Comprehensive RAG Benchmark: KDD Cup 2-67e3e3

πŸ’¬ Welcome to the Meta Comprehensive RAG Benchmark Challenge

24 days ago

Join the Meta Comprehensive RAG Benchmark Challenge 2024, to build more accurate LLMs that can access dynamic external data, changing how AI systems understand and increase their reliability.

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) such as ChatGPT by integrating latest information, overcoming the limitations associated with outdated or incorrect data. RAG updates the static knowledge base of LLMs, ensuring responses remain relevant and precise. RAG incorporates external knowledge by accessing extensive databases, allowing for answers that reflect up-to-date references. The use of external information improves the quality of communication and provides relevant and accurate information.

:bookmark: The Challenge
The CRAB 2024 challenge evaluates RAG models across diverse fields, including finance, movie, music, sports and encyclopedia. The challenge includes tasks such as summarizing web content, enriching responses with structured data from knowledge graphs and APIs, and an integrated task that merges these elements with noise reduction. These tasks aim to improve QA systems’ capacity to deliver accurate and relevant answers.

What Makes CRAB Standout?

  • Wide-Ranging Domains: From the music to finance, CRAB covers five separate domains to ensure a broad testing ground.
  • Diverse Question Types: Whether it’s the latest sports scores or historical facts, CRAB challenges systems over five types of question types.
  • Innovative Tasks: Participants will tackle three distinct sub-tasks, from summarizing information to augmenting questions with web and knowledge graph data, all the way to end-to-end RAG processing.

:date: Timeline

  • Phase 1 Start Date: 15th March, 2024 23:55 UTC
  • Team Freeze Deadline: 1st April, 2024 23:55 UTC
  • Phase 2 Start Date: 22nd May, 2023 23:55 UTC
  • End Date: 10th July, 2024 23:55 UTC
  • Winner Notification: 15th July, 2024
  • Winner Announcement: 26th August, 2024 (KDD Cup Winners)

:moneybag: Prizes
The challenge prioritizes excellence and innovation, rewarding the top three teams with:

:1st_place_medal: First Place: $3,000 plus Meta Smart Glasses
:2nd_place_medal: Second Place: $1,500
:3rd_place_medal: Third Place: $500

Winners will present their innovative work at the KDD Cup Workshop in Barcelona, Spain, joining the elite in data mining and AI research.

Join a community of innovators, exchanging insights, forming partnerships, and leading the charge in redefining AI capabilities through the Meta Comprehensive RAG Benchmark Challenge 2024. Take this chance to lead in AI innovation, developing solutions that enhance information retrieval.

πŸ’¬ Feedback & Suggestions

24 days ago

We are constantly trying to improve this challenge for you and would appreciate any feedback you might have! :raised_hands:

Please reply to this thread with your suggestions and feedback on making the challenge better for you!

  • What have been your major pain points so far?
  • What would you like to see improved?

All The Best!
Team CRAB

Commonsense Persona-Grounded Dialogue Chall-1f6f43

Important: 24-Hour Extension to Challenge Deadline

15 days ago

Hello all,

To accommodate for the time lost owing to technical difficulties encountered with GPU nodes, we are extending the deadline for the challenge by 24 hours. The challenge will now conclude on March 16, 2024, at 23:59 UTC.

All the best,
Team CPD

Task 1: Commonsense Dialogue Response Generation

Updates to Task 1 Metrics

22 days ago

Hello tomtom,

  1. Yes, at least five teams from the final leaderboard will be selected for human evaluation. There is a possibility that the number of teams selected may increase, which will be communicated at a later date.
  2. We are currently reviewing the criteria for human evaluation. Although we intend to adhere to the existing criteria, we may make adjustments if we encounter any difficulties during the review process. Further details are explained over here: Task 1: Commonsense Dialogue Response Generation.
  3. Yes, if your submission ranks at the top of the leaderboard by the end of the challenge, we will contact you via email to collect any datasets you have gathered or created for training your model.

Hope this answers your queries.

snehananavati has not provided any information yet.