Intermediate layer matching is shown as an effective approach for improving knowledge distillation (KD).
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.
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one.
Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.
Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning.
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.
Then, we explain second-order methods including Newton's method for unconstrained, equality constrained, and inequality constrained problems....
Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications.
We present our KroneckerBERT, a compressed version of the BERT_BASE model obtained using this framework.
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge of a large neural network into a smaller one.
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.
This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections.
One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction.
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.
In this paper, we propose Legendre Deep Neural Network (LDNN) for solving nonlinear Volterra Fredholm Hammerstein integral equations (VFHIEs).
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values.
We start with reviewing the history of kernels in functional analysis and machine learning.
Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection.
We exploit a semi-supervised approach based on KD to train a model on augmented data.
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.
In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic.
Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced.
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.
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.
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.
We describe a neural-based method for generating exact or approximate solutions to differential equations in the form of mathematical expressions.
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.
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.
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.
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.
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.
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.
In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems.
The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis.
In this paper we propose a novel method, Improved Word Vectors (IWV), which increases the accuracy of pre-trained word embeddings in sentiment analysis.
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.
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.
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.
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.
In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation.
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.
In this work, we present Velox, a new component of the Berkeley Data Analytics Stack.
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.
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.
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.
To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images.
In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary.