Kathmandu, Nepal

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+977-9851158281

10 AM – 5 PM, Kathmandu

Research

Recently, our co-founder, Mr. Tej Bahadur shahi and his team at Central Queensland University, Australia, published a research output on “Stock Price Forecasting with Deep Learning: A Comparative Study”. It is a time series forecasting problem on stock market domain, focused on Nepalese stock market. Here they proposed a co-operative deep learning architecture for accurate prediction of stock price, one day ahead. It might be a good start point to new researcher who wants to work on time series forecasting using deep learning framework. The detail of paper can be found at : https://doi.org/10.3390/math8091441

If your research benefits form this work, please cite this paper as: Shahi, T.B. ; Shrestha, A .; Neupane, A. ; Guo, W. Stock Price Forecasting with Deep Learning: A Comparative Study. Mathematics 2020, 8, 1441

Classification

  1. High-Precision Phrase-Based Document Classification on a Modern Scale (Paper) LinkedIn

  2. Chimera: Large-scale Classification using Machine Learning, Rules, and Crowdsourcing (Paper) WalmartLabs

  3. Large-scale Item Categorization for e-Commerce (Paper) DianPing, eBay

  4. Large-scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks (Paper) NAVER

  5. Categorizing Products at Scale Shopify

  6. Learning to Diagnose with LSTM Recurrent Neural Networks (Paper) Google

  7. Discovering and Classifying In-app Message Intent at Airbnb Airbnb

  8. How We Built the Good First Issues Feature GitHub

  9. Teaching Machines to Triage Firefox Bugs Mozilla

  10. Testing Firefox More Efficiently with Machine Learning Mozilla

  11. Using ML to Subtype Patients Receiving Digital Mental Health Interventions (Paper) Microsoft

  12. Prediction of Advertiser Churn for Google AdWords (Paper) Google

  13. Scalable Data Classification for Security and Privacy (Paper) Facebook

  1. Using Machine Learning to Predict Value of Homes On Airbnb

  2. Using Machine Learning to Predict the Value of Ad Requests Twitter

  3. Open-Sourcing Riskquant, a Library for Quantifying Risk (Code) NetFlix

  1. Amazon.com Recommendations: Item-to-Item Collaborative Filtering (Paper) Amazon

  2. Temporal-Contextual Recommendation in Real-Time (Paper) Amazon

  3. Recommending Complementary Products in E-Commerce Push Notifications (Paper) Alibaba

  4. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba (Paper) Alibaba

  5. TPG-DNN: A Method for User Intent Prediction with Multi-task Learning (Paper) Alibaba

  6. Session-based Recommendations with Recurrent Neural Networks (Paper) Telefonica

  7. How 20th Century Fox uses ML to predict a movie audience (Paper) 20th Century Fox

  8. Deep Neural Networks for YouTube Recommendations YouTube

  9. Personalized Recommendations for Experiences Using Deep Learning TripAdvisor

  10. E-commerce in Your Inbox: Product Recommendations at Scale Yahoo

  11. Product Recommendations at Scale (Paper) Yahoo

  12. Powered by AI: Instagram’s Explore recommender system Facebook

  13. Netflix Recommendations: Beyond the 5 stars (Part 1 (Part 2) Netflix

  14. Learning a Personalized Homepage Netflix

  15. Artwork Personalization at Netflix Netflix

  16. To Be Continued: Helping you find shows to continue watching on Netflix Netflix

  17. Calibrated Recommendations (Paper) Netflix

  18. Food Discovery with Uber Eats: Recommending for the Marketplace Uber

  19. Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations Uber

  20. How Music Recommendation Works — And Doesn’t Work Spotify

  21. Music recommendation at Spotify Spotify

  22. Recommending Music on Spotify with Deep Learning Spotify

  23. For Your Ears Only: Personalizing Spotify Home with Machine Learning Spotify

  24. Reach for the Top: How Spotify Built Shortcuts in Just Six Months Spotify

  25. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits (Paper) Spotify

  26. The Evolution of Kit: Automating Marketing Using Machine Learning Shopify

  27. Using Machine Learning to Predict what File you Need Next (Part 1) Dropbox

  28. Using Machine Learning to Predict what File you Need Next (Part 2) Dropbox

  29. Personalized Recommendations in LinkedIn Learning LinkedIn

  30. A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 1) LinkedIn

  31. A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 2) LinkedIn

  32. Learning to be Relevant: Evolution of a Course Recommendation System (PAPER NEEDED)LinkedIn

  33. How TikTok recommends videos #ForYou ByteDance

  34. A Meta-Learning Perspective on Cold-Start Recommendations for Items (Paper) Twitter

  35. Zero-Shot Heterogeneous Transfer Learning from RecSys to Cold-Start Search Retrieval (Paper) Google

  36. Improved Deep & Cross Network for Feature Cross Learning in Web-scale LTR Systems (Paper) Google

  37. Personalized Channel Recommendations in Slack

     

  1. Amazon Search: The Joy of Ranking Products (Paper, Video, Code) Amazon

  2. Why Do People Buy Seemingly Irrelevant Items in Voice Product Search? (Paper) Amazon

  3. How Lazada Ranks Products to Improve Customer Experience and Conversion Lazada

  4. Using Deep Learning at Scale in Twitter’s Timelines Twitter

  5. Machine Learning-Powered Search Ranking of Airbnb Experiences Airbnb

  6. Applying Deep Learning To Airbnb Search (Paper) Airbnb

  7. Managing Diversity in Airbnb Search (Paper) Airbnb

  8. Ranking Relevance in Yahoo Search (Paper) Yahoo

  9. An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy (Paper) Etsy

  10. Learning to Rank Personalized Search Results in Professional Networks (Paper) LinkedIn

  11. Entity Personalized Talent Search Models with Tree Interaction Features (Paper) LinkedIn

  12. In-session Personalization for Talent Search (Paper) LinkedIn

  13. The AI Behind LinkedIn Recruiter search and recommendation systems LinkedIn

  14. Quality Matches Via Personalized AI for Hirer and Seeker Preferences LinkedIn

  15. AI at Scale in Bing Microsoft

  16. Query Understanding Engine in Traveloka Universal Search Traveloka

  17. The Secret Sauce Behind Search Personalisation GoJek

  18. Food Discovery with Uber Eats: Building a Query Understanding Engine Uber

  19. Neural Code Search: ML-based Code Search Using Natural Language Queries Facebook

  20. Bayesian Product Ranking at Wayfair Wayfair

  21. COLD: Towards the Next Generation of Pre-Ranking System (Paper) Alibaba

  22. Understanding Searches Better Than Ever Before (Paper) Google

  1. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Paper) Alibaba

  2. Embeddings@Twitter Twitter

  3. Listing Embeddings in Search Ranking (Paper) Airbnb

  4. Understanding Latent Style Stitch Fix

  5. Towards Deep and Representation Learning for Talent Search at LinkedIn (Paper) LinkedIn

  6. Vector Representation Of Items, Customer And Cart To Build A Recommendation System (Paper) Sears

  7. Machine Learning for a Better Developer Experience Netflix

  8. Announcing ScaNN: Efficient Vector Similarity Search (Paper, Code) Google

  1. Abusive Language Detection in Online User Content (Paper) Yahoo

  2. How Natural Language Processing Helps LinkedIn Members Get Support Easily LinkedIn

  3. Building Smart Replies for Member Messages LinkedIn

  4. DeText: A deep NLP Framework for Intelligent Text Understanding (Code) LinkedIn

  5. Smart Reply: Automated Response Suggestion for Email (Paper) Google

  6. Gmail Smart Compose: Real-Time Assisted Writing (Paper) Google

  7. SmartReply for YouTube Creators Google

  8. Using Neural Networks to Find Answers in Tables (Paper) Google

  9. A Scalable Approach to Reducing Gender Bias in Google Translate Google

  10. Assistive AI Makes Replying Easier Microsoft

  11. AI Advances to Better Detect Hate Speech Facebook

  12. A State-of-the-Art Open Source Chatbot (Paper) Facebook

  13. A Highly Efficient, Real-Time Text-to-Speech System Deployed on CPUs Facebook

  14. Deep Learning to Translate Between Programming Languages (Paper, Code) Facebook

  15. Deploying Lifelong Open-Domain Dialogue Learning (Paper) Facebook

  16. Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting (Paper) Amazon

  17. How Gojek Uses NLP to Name Pickup Locations at Scale GoJek

  18. Give Me Jeans not Shoes: How BERT Helps Us Deliver What Clients Want Stitch Fix

  19. The State-of-the-art Open-Domain Chatbot in Chinese and English (Paper) Baidu

  20. PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization (Paper, Code) Google

  21. Photon: A Robust Cross-Domain Text-to-SQL System (Paper) (Demo) Salesforce

  22. Applying Topic Modeling to Improve Call Center Operations RICOH

  1. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (Paper)Alibaba

  2. Search-based User Interest Modeling with Sequential Behavior Data for CTR Prediction (Paper) Alibaba

  3. Learning to Diagnose with LSTM Recurrent Neural Networks (Paper) Google

  4. Deep Learning for Understanding Consumer Histories (Paper) Zalando

  5. Continual Prediction of Notification Attendance with Classical and Deep Networks (Paper) Telefonica

  6. Using Recurrent Neural Network Models for Early Detection of Heart Failure Onset (Paper) Sutter Health

  7. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (Paper) Sutter Health

  8. How Duolingo uses AI in every part of its app Duolingo

  9. Leveraging Online Social Interactions For Enhancing Integrity at Facebook (Paper) Facebook

  1. Forecasting at Uber: An Introduction Uber

  2. Engineering Extreme Event Forecasting at Uber with RNN Uber

  3. Transforming Financial Forecasting with Data Science and Machine Learning at Uber Uber

  4. Under the Hood of Gojek’s Automated Forecasting Tool GoJek

  1. Categorizing Listing Photos at Airbnb Airbnb

  2. Amenity Detection and Beyond — New Frontiers of Computer Vision at Airbnb Airbnb

  3. Powered by AI: Advancing product understanding and building new shopping experiences Facebook

  4. Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning Dropbox

  5. How we Improved Computer Vision Metrics by More Than 5% Only by Cleaning Labelling Errors Deepomatic

  6. A Neural Weather Model for Eight-Hour Precipitation Forecasting (Paper) Google

  7. Machine Learning-based Damage Assessment for Disaster Relief (Paper) Google

  8. RepNet: Counting Repetitions in Videos (Paper) Google

  9. Converting Text to Images for Product Discovery (Paper) Amazon

  10. How Disney Uses PyTorch for Animated Character Recognition Disney

  11. Image Captioning as an Assistive Technology (Video) IBM

  12. AI for AG: Production machine learning for agriculture Blue River

  13. AI for Full-Self Driving at Tesla Tesla

  14. On-device Supermarket Product Recognition Google

  15. Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings (Paper) Google

  1. Deep Reinforcement Learning for Sponsored Search Real-time Bidding (Paper) Alibaba

  2. Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning (Paper) Alibaba

  3. Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (Paper) Alibaba

  4. Productionizing Deep Reinforcement Learning with Spark and MLflow Zynga

  5. Deep Reinforcement Learning in Production Part1 Part 2 Zynga

  6. Building AI Trading Systems Denny Britz

  1. Detecting Performance Anomalies in External Firmware Deployments Netflix

  2. Detecting and Preventing Abuse on LinkedIn using Isolation Forests (Code) LinkedIn

  3. Preventing Abuse Using Unsupervised Learning LinkedIn

  4. The Technology Behind Fighting Harassment on LinkedIn LinkedIn

  5. Uncovering Insurance Fraud Conspiracy with Network Learning (Paper) Ant Financial

  6. How Does Spam Protection Work on Stack Exchange? Stack Exchange

  7. Auto Content Moderation in C2C e-Commerce Mercari

  8. Blocking Slack Invite Spam With Machine Learning Slack

  9. Cloudflare Bot Management: Machine Learning and More Cloudflare

  10. Anomalies in Oil Temperature Variations in a Tunnel Boring Machine SENER

  11. Using Anomaly Detection to Monitor Low-Risk Bank Customers Rabobank

  1. Building The LinkedIn Knowledge Graph LinkedIn

  2. Retail Graph — Walmart’s Product Knowledge Graph Walmart

  3. Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations Uber

  4. AliGraph: A Comprehensive Graph Neural Network Platform (Paper) Alibaba

  5. Scaling Knowledge Access and Retrieval at Airbnb Airbnb

  6. Traffic Prediction with Advanced Graph Neural Networks DeepMind

  1. How Trip Inferences and Machine Learning Optimize Delivery Times on Uber Eats Uber

  2. Next-Generation Optimization for Dasher Dispatch at DoorDash DoorDash

  3. Matchmaking in Lyft Line (Part 1) (Part 2) (Part 3) Lyft

  4. The Data and Science behind GrabShare Carpooling (PAPER NEEDED) Grab

  5. Optimization of Passengers Waiting Time in Elevators Using Machine Learning Thyssen Krupp AG

  1. Unsupervised Extraction of Attributes and Their Values from Product Description (Paper) Rakuten

  2. Information Extraction from Receipts with Graph Convolutional Networks Nanonets

  3. Using Machine Learning to Index Text from Billions of Images Dropbox

  4. Extracting Structured Data from Templatic Documents (Paper) Google

  1. Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale (Paper) Google

  2. Osprey: Weak Supervision of Imbalanced Extraction Problems without Code (Paper) Intel

  3. Overton: A Data System for Monitoring and Improving Machine-Learned Products (Paper) Apple

  4. Bootstrapping Conversational Agents with Weak Supervision (Paper) IBM

  1. Better Language Models and Their Implications (Paper)OpenAI

  2. Language Models are Few-Shot Learners (Paper) (GPT-3 Blog post) OpenAI

  3. Image GPT (Paper, Code) OpenAI

  4. Deep Learned Super Resolution for Feature Film Production (Paper) Pixar

  1. GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce (Paper) Paper

  1. The Reusable Holdout: Preserving Validity in Adaptive Data Analysis (Paper) Google

  2. Detecting Interference: An A/B Test of A/B Tests LinkedIn

  3. Experimenting to Solve Cramming Twitter

  4. Announcing a New Framework for Designing Optimal Experiments with Pyro (Paper) (Paper) Uber

  5. Enabling 10x More Experiments with Traveloka Experiment Platform Traveloka

  6. Large Scale Experimentation at Stitch Fix (Paper) Stitch Fix

  7. Multi-Armed Bandits and the Stitch Fix Experimentation Platform Stitch Fix

  8. Modeling Conversion Rates and Saving Millions Using Kaplan-Meier and Gamma Distributions (Code) Better

  9. Computational Causal Inference at Netflix (Paper) Netflix

  10. Key Challenges with Quasi Experiments at Netflix Netflix

  1. Building Inclusive Products Through A/B Testing (Paper) LinkedIn

  2. LiFT: A Scalable Framework for Measuring Fairness in ML Applications (Paper) LinkedIn

  1. Practical Recommendations for Gradient-Based Training of Deep Architectures (Paper) Yoshua Bengio

  2. Machine Learning: The High Interest Credit Card of Technical Debt (Paper) (Paper) Google

  3. Rules of Machine Learning: Best Practices for ML Engineering Google

  4. On Challenges in Machine Learning Model Management Amazon

  5. Machine Learning in Production: The Booking.com Approach Booking

  6. 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (Paper) Booking

  7. Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department Stitch Fix

  8. Beware the Data Science Pin Factory: The Power of the Full-Stack Data Science Generalist Stitch Fix

  9. Successes and Challenges in Adopting Machine Learning at Scale at a Global Bank Rabobank