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snehananavati
Sneha Nanavati

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Challenges Entered

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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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Participant Rating

Task 1: Commonsense Dialogue Response Generation

Question on Round System

2 days ago

Hello, you are eligible to submit for the ongoing evaluation round (which in this case would be Round 2).

Commonsense Persona-Grounded Dialogue Chall-1f6f43

🚨 Important Update: Challenge Deadline Extended & Update to Rules

10 days ago

Hello all,

The current challenge rules outline that the challenge end dates to be 1st March, 2024.

To give participants additional time to explore and share improved solutions, we have extended the challenge deadline by two weeks. The new challenge end date is 15th March 2024, 23:55 UTC.

For consistency, we are updating the Challenge Rules to reflect the same, and this will require all the participants to re-accept the rules (by Clicking on the Participate button on the Challenge Overview page) before they can make any further submissions.

Please also make note that the daily submission limit has been increased to 10 submissions per day.

Hope this change enables you to share more of your amazing work. We can’t wait to see all the incredible submissions you’ll bring!

All the best!
Team CPD Challenge

⏰ Daily Submission Limit Increased! πŸ“ˆ

14 days ago

Great news for all participants! We’re thrilled to announce that the daily submission limit has been increased to 10 submissions per day.

Hope this change enables you to share more of your amazing work. We can’t wait to see all the incredible submissions you’ll bring!

πŸ“šβœ¨πŸ“• Resources for Task1 & Task2: Research Papers, Models and More

28 days ago

Hello all,

To help you ace this challenge, we are sharing a list of resources for Task 1 and Task 2.

LLM leaderboard Open LLM Leaderboard - a Hugging Face Space by HuggingFaceH4

  • participants can choose and change LLM from β€œBaseline LLM-Phi2-Prompt” which is currently using Phi-2 (microsoft/phi-2 Β· Hugging Face)
  • it’s better to choose models with about 7B or less parameters in terms of resource restrictions
  • participants can change LLM, modify prompts, and/or fine-tune LLMs

Dialogue dataset & models

  1. Topcial-Chat
  1. OpenAssistant
  1. LMSYS-CHAT-1M

Harry Potter Dialogue (HPD)

Commonsense Knowledge Graph (CSKG):

Commonsense Knowledge Construction

Make your first submission for Prompt Engineer Track in Task 1 with the help of this easy to follow video: https://www.youtube.com/watch?v=5I2DoCiG0kc

Do these resources give you new ideas for your solution? How will you use these resources? Share your ideas or questions down below! :point_down:

Generative Interior Design Challenge 2024

πŸ–ΌοΈ Welcome to Generative Interior Design Challenge 2024

30 days ago

REVOLUTIONISING INTERIOR DESIGN WITH AI

Join the novel Generative Interior Design 2024 a pioneering challenge at the crossroads of computer vision and AI. This innovative competition leverages the power of AI in interior design, where your algorithms will turn images of empty rooms into fully designed spaces guided by text descriptions.

Use your skills to bring life into empty spaces, transforming them into visions of beauty and functionality with just a few lines of code and a burst of imagination. Participate to revolutionise the way we envision living spaces.

:bookmark_tabs: The Task

Develop an algorithm that transforms an image empty room into a furnished one by interpreting text prompts.

This tool aims to revolutionise interior design, architecture, and home planning, offering a cutting-edge solution for visualising designs in new constructions, renovations, and rentals bridging the gap between ideas and reality. Dive into this challenge to reshape how we see and create living spaces!

:date: Timeline

  • Challenge Start: February 1, 2024 UTC: 23:55
  • Challenge End: April 1, 2024 UTC: 23:55
  • Winners’ Presentation at MCS: April 16, 2024

:moneybag: Prizes

This challenge gathers a prize pool of USD 15,000. The top three teams or participants will be rewarded as follows:

  • :1st_place_medal: 1st on the leaderboard: USD 7000
  • :2nd_place_medal: 2nd on the leaderboard: USD 5000
  • :3rd_place_medal: 3rd on the leaderboard: USD 3000

The winners of the challenge will be invited to present their solutions at the Machines Can See Summit 2024 in Dubai and will be awarded a travel grant.

Join a thriving community eager to exchange ideas, team up, and enrich each other’s experience in this captivating challenge. Be part of a group of thinkers and innovators. Exchange resources, engage in thought-provoking discussions, and create winning teams.


πŸ’¬ Feedback & Suggestions

About 1 month 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!

πŸ‘₯ Looking for teammates?

About 1 month 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!

MosquitoAlert Challenge 2023

πŸ† How did MPWARE topped the leaderboard? 🦟

22 days ago

Today, we’re diving into the story of Eric, a seasoned software engineer, whose journey through the MosquitoAlert Challenge is an example of the power of continuous learning and practical application in the realm of artificial intelligence. Eric’s unique approach to tackling machine learning challenges combines effective problem-solving with tangible, hands-on experience. This challenge has enhanced his expertise in areas such as algorithm development, data analysis, and model optimization. Let’s hear how he ranked third in the competition and dive deeper into his solutions.

:mosquito: Discovering the MosquitoAlert Challenge

It all started when Eric was looking for a new project to sink his teeth into during the summer and found the MosquitoAlert challenge. The detailed problem statement, coupled with an engaging townhall event featuring seasoned experts, sparked his curiosity.

:mag: Technical Exploration

In this challenge, Team MPWare led by Eric embarked on a two-pronged solution to efficiently detect and classify mosquitoes.

Stage 1: Tackling Mosquito Detection Using Yolo

The first stage was critical for the initial identification and localization of mosquitoes in images, employing a Yolo-based model to detect mosquito bounding boxes.

Stage 2: The Ensemble Approach for Mosquito Classification

In the second stage, the team utilized an ensemble of two distinct models for the classification task. One model was built on the Vision Transformer (ViT) architecture, renowned for its efficacy in image recognition tasks. The second model leveraged the EfficientNetv2 architecture, celebrated for its efficiency and accuracy in image classification.

:point_right: To read the complete breakdown of MPWare’s solution read the blog over here

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