Saeed Amizadeh

According to our database, Saeed Amizadeh authored at least 19 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Weakly-supervised Audio Separation via Bi-modal Semantic Similarity.

Tanvir Mahmud, Saeed Amizadeh, Kazuhito Koishida, Diana Marculescu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2021

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele.

Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

Proc. VLDB Endow., 2021

2020

Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning".

Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, Kazuhito Koishida

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.

Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

CoRR, 2019

Machine Learning at Microsoft with ML .NET.

Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupré, Vadim Eksarevskiy, Eric Erhardt, Costin Eseanu, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Shon Katzenberger, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Sarthak Shah, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu

CoRR, 2019

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers.

Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

CoRR, 2019

Machine Learning at Microsoft with ML.NET.

Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupré, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Coded Elastic Computing.

Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Markus Weimer

Proceedings of the IEEE International Symposium on Information Theory, 2019

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach.

Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Towards a Learning Optimizer for Shared Clouds.

Chenggang Wu, Alekh Jindal, Saeed Amizadeh, Hiren Patel, Wangchao Le, Shi Qiao, Sriram Rao

Proc. VLDB Endow., 2018

2015

Generic and Scalable Framework for Automated Time-series Anomaly Detection.

Nikolay Laptev, Saeed Amizadeh, Ian Flint

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series.

George D. Montañez, Saeed Amizadeh, Nikolay Laptev

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2013

The Bregman Variational Dual-Tree Framework.

Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012

Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning.

Hadi Firouzi, Majid Nili Ahmadabadi, Babak Nadjar Araabi, Saeed Amizadeh, Maryam S. Mirian, Roland Siegwart

IEEE Trans. Auton. Ment. Dev., 2012

Factorized Diffusion Map Approximation.

Saeed Amizadeh, Hamed Valizadegan, Milos Hauskrecht

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation.

Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Sampling Strategies to Evaluate the Performance of Unknown Predictors.

Hamed Valizadegan, Saeed Amizadeh, Milos Hauskrecht

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

2011

An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics.

Saeed Amizadeh, Shuguang Wang, Milos Hauskrecht

Proceedings of the IJCAI 2011, 2011

2010

Latent Variable Model for Learning in Pairwise Markov Networks.

Saeed Amizadeh, Milos Hauskrecht

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010