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no code implementations • EMNLP 2021 • Yimeng Wu, Mehdi Rezagholizadeh, Abbas Ghaddar, Md Akmal Haidar, Ali Ghodsi

Intermediate layer matching is shown as an effective approach for improving knowledge distillation (KD).

no code implementations • Findings (EMNLP) 2021 • Peng Lu, Abbas Ghaddar, Ahmad Rashid, Mehdi Rezagholizadeh, Ali Ghodsi, Philippe Langlais

Knowledge Distillation (KD) is extensively used in Natural Language Processing to compress the pre-training and task-specific fine-tuning phases of large neural language models.

no code implementations • Findings (EMNLP) 2021 • Tianda Li, Ahmad Rashid, Aref Jafari, Pranav Sharma, Ali Ghodsi, Mehdi Rezagholizadeh

Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one.

no code implementations • 26 Nov 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.

no code implementations • 18 Oct 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning.

no code implementations • 16 Oct 2021 • Mehdi Rezagholizadeh, Aref Jafari, Puneeth Salad, Pranav Sharma, Ali Saheb Pasand, Ali Ghodsi

A case in point is that the best performing checkpoint of the teacher might not necessarily be the best teacher for training the student in KD.

no code implementations • 5 Oct 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, we explain second-order methods including Newton's method for unconstrained, equality constrained, and inequality constrained problems....

no code implementations • WNUT (ACL) 2021 • Shivendra Bhardwaj, Abbas Ghaddar, Ahmad Rashid, Khalil Bibi, Chengyang Li, Ali Ghodsi, Philippe Langlais, Mehdi Rezagholizadeh

Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications.

no code implementations • 13 Sep 2021 • Marzieh S. Tahaei, Ella Charlaix, Vahid Partovi Nia, Ali Ghodsi, Mehdi Rezagholizadeh

We present our KroneckerBERT, a compressed version of the BERT_BASE model obtained using this framework.

no code implementations • 13 Sep 2021 • Tianda Li, Ahmad Rashid, Aref Jafari, Pranav Sharma, Ali Ghodsi, Mehdi Rezagholizadeh

Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge of a large neural network into a smaller one.

no code implementations • 25 Aug 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

We start with UMAP algorithm where we explain probabilities of neighborhood in the input and embedding spaces, optimization of cost function, training algorithm, derivation of gradients, and supervised and semi-supervised embedding by UMAP.

no code implementations • 9 Aug 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections.

no code implementations • 26 Jul 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, we introduce the structures of BM and RBM.

1 code implementation • Software Impacts 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction.

no code implementations • 29 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants.

no code implementations • 27 Jun 2021 • Zeinab Hajimohammadi, Kourosh Parand, Ali Ghodsi

In this paper, we propose Legendre Deep Neural Network (LDNN) for solving nonlinear Volterra Fredholm Hammerstein integral equations (VFHIEs).

2 code implementations • 27 Jun 2021 • Mojtaba Valipour, Bowen You, Maysum Panju, Ali Ghodsi

Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values.

no code implementations • 15 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

We start with reviewing the history of kernels in functional analysis and machine learning.

no code implementations • 3 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection.

no code implementations • Findings (ACL) 2021 • Ehsan Kamalloo, Mehdi Rezagholizadeh, Peyman Passban, Ali Ghodsi

We exploit a semi-supervised approach based on KD to train a model on augmented data.

no code implementations • EACL 2021 • Aref Jafari, Mehdi Rezagholizadeh, Pranav Sharma, Ali Ghodsi

Knowledge distillation (KD) is a prominent model compression technique for deep neural networks in which the knowledge of a trained large teacher model is transferred to a smaller student model.

1 code implementation • 4 Apr 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic.

no code implementations • 4 Jan 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced.

no code implementations • 1 Jan 2021 • Kourosh Parand, Zeinab Hajimohammadi, Ali Ghodsi

In particular, Volterra–Fredholm–Hammerstein integral equations are the main type of these integral equations and researchers are interested in investigating and solving these equations.

no code implementations • 1 Jan 2021 • Aref Jafari, Mehdi Rezagholizadeh, Ali Ghodsi

Augmenting the training set by adding this auxiliary improves the performance of KD significantly and leads to a closer match between the student and the teacher.

1 code implementation • 22 Nov 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE.

no code implementations • 17 Nov 2020 • Benyamin Ghojogh, Ali Ghodsi

Thereafter, we introduce the Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) as the stacks of encoders and decoders of transformer, respectively.

no code implementations • 12 Nov 2020 • Maysum Panju, Kourosh Parand, Ali Ghodsi

We describe a neural-based method for generating exact or approximate solutions to differential equations in the form of mathematical expressions.

no code implementations • 4 Nov 2020 • Maysum Panju, Ali Ghodsi

When neural networks are used to solve differential equations, they usually produce solutions in the form of black-box functions that are not directly mathematically interpretable.

1 code implementation • 22 Sep 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space.

1 code implementation • 17 Sep 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, Sammon mapping, Isomap, and kernel Isomap are explained.

no code implementations • 30 Jun 2020 • Aref Jafari, Ali Ghodsi

This has been accomplished by defining an embedding method for the position of all members of a coreference cluster in a document and resolving all of them for a given mention.

1 code implementation • 17 Apr 2019 • Rui Qiao, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li, Ali Ghodsi

Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy.

no code implementations • 18 Dec 2018 • Ershad Banijamali, Amir-Hossein Karimi, Ali Ghodsi

We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information of the observations regarding the label variable is preserved.

1 code implementation • 7 Nov 2018 • Amir-Hossein Karimi, Alexander Wong, Ali Ghodsi

While stochastic approximation strategies have been explored for unsupervised dimensionality reduction to tackle this challenge, such approaches are not well-suited for accelerating computational speed for supervised dimensionality reduction.

no code implementations • 23 Jul 2018 • Seyed Mahdi Rezaeinia, Ali Ghodsi, Rouhollah Rahmani

In the Text Classification areas of Sentiment Analysis, Subjectivity/Objectivity Analysis, and Opinion Polarity, Convolutional Neural Networks have gained special attention because of their performance and accuracy.

no code implementations • 15 Dec 2017 • Ion Stoica, Dawn Song, Raluca Ada Popa, David Patterson, Michael W. Mahoney, Randy Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David Culler, Pieter Abbeel

With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.

no code implementations • 24 Nov 2017 • Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi, Mohammad Ghavamzadeh

In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems.

no code implementations • 24 Nov 2017 • Ershad Banijamali, Amir-Hossein Karimi, Alexander Wong, Ali Ghodsi

The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis.

1 code implementation • 23 Nov 2017 • Seyed Mahdi Rezaeinia, Ali Ghodsi, Rouhollah Rahmani

In this paper we propose a novel method, Improved Word Vectors (IWV), which increases the accuracy of pre-trained word embeddings in sentiment analysis.

no code implementations • 15 Oct 2017 • Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi

We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise.

no code implementations • 25 Aug 2017 • Shima Kamyab, Ali Ghodsi, S. Zohreh Azimifar

Inverse rendering in a 3D format denoted to recovering the 3D properties of a scene given 2D input image(s) and is typically done using 3D Morphable Model (3DMM) based methods from single view images.

no code implementations • 7 Apr 2017 • Ershad Banijamali, Ali Ghodsi

Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition.

no code implementations • 10 Feb 2017 • Ershad Banijamali, Ali Ghodsi, Pascal Poupart

The model consists of K networks that are trained together to learn the underlying distribution of a given data set.

no code implementations • 10 May 2016 • Ershad Banijamali, Ali Ghodsi

Then, we map the data to lower-dimensional space using a linear transformation such that the dependency between the transformed data and the assigned labels is maximized.

no code implementations • 25 Apr 2016 • Mehrdad J. Gangeh, Safaa M. A. Bedawi, Ali Ghodsi, Fakhri Karray

The proposed method benefits from the supervisory information by learning the dictionary in a space where the dependency between the data and class labels is maximized.

no code implementations • 6 Mar 2015 • Mehrdad J. Gangeh, Ali Ghodsi

In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation.

no code implementations • 20 Feb 2015 • Mehrdad J. Gangeh, Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel

This review provides a broad, yet deep, view of the state-of-the-art methods for S-DLSR and allows for the advancement of research and development in this emerging area of research.

2 code implementations • 12 Sep 2014 • Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael. I. Jordan

In this work, we present Velox, a new component of the Berkeley Data Analytics Stack.

Databases

no code implementations • 24 Dec 2013 • Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel

This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B.

no code implementations • 24 Dec 2013 • Ahmed K. Farahat, Ahmed Elgohary, Ali Ghodsi, Mohamed S. Kamel

The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized.

no code implementations • 1 Feb 2013 • Peter Bailis, Aaron Davidson, Alan Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica

To minimize network latency and remain online during server failures and network partitions, many modern distributed data storage systems eschew transactional functionality, which provides strong semantic guarantees for groups of multiple operations over multiple data items.

Databases

no code implementations • 12 Jul 2012 • Mehrdad J. Gangeh, Ali Ghodsi, Mohamed S. Kamel

To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images.

no code implementations • 10 Jul 2012 • Mehrdad J. Gangeh, Ali Ghodsi, Mohamed S. Kamel

In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary.

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