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03/24/2023
Mathematical Challenges in Deep Learning
Deep models are dominating the artificial intelligence (AI) industry sin...
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01/17/2023
Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm
Deep neural networks are getting larger. Their implementation on edge an...
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01/04/2023
On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats
Deep learning models are dominating almost all artificial intelligence t...
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12/22/2022
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models
With the advent of deep learning application on edge devices, researcher...
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12/22/2022
Training Integer-Only Deep Recurrent Neural Networks
Recurrent neural networks (RNN) are the backbone of many text and speech...
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12/20/2022
KronA: Parameter Efficient Tuning with Kronecker Adapter
Fine-tuning a Pre-trained Language Model (PLM) on a specific downstream ...
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10/12/2022
SeKron: A Decomposition Method Supporting Many Factorization Structures
While convolutional neural networks (CNNs) have become the de facto stan...
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09/20/2022
Integer Fine-tuning of Transformer-based Models
Transformer based models are used to achieve state-of-the-art performanc...
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08/20/2022
DenseShift: Towards Accurate and Transferable Low-Bit Shift Network
Deploying deep neural networks on low-resource edge devices is challengi...
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07/18/2022
Is Integer Arithmetic Enough for Deep Learning Training?
The ever-increasing computational complexity of deep learning models mak...
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02/18/2022
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks
Recent efforts in deep learning show a considerable advancement in redes...
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10/25/2021
Demystifying and Generalizing BinaryConnect
BinaryConnect (BC) and its many variations have become the de facto stan...
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10/15/2021
Kronecker Decomposition for GPT Compression
GPT is an auto-regressive Transformer-based pre-trained language model w...
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09/29/2021
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition
Modern Convolutional Neural Network (CNN) architectures, despite their s...
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09/20/2021
iRNN: Integer-only Recurrent Neural Network
Recurrent neural networks (RNN) are used in many real-world text and spe...
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09/13/2021
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation
The development of over-parameterized pre-trained language models has ma...
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07/07/2021
S^3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Shift neural networks reduce computation complexity by removing expensiv...
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05/11/2021
A Twin Neural Model for Uplift
Uplift is a particular case of conditional treatment effect modeling. Su...
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06/09/2020
Tensor train decompositions on recurrent networks
Recurrent neural networks (RNN) such as long-short-term memory (LSTM) ne...
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06/08/2020
Clustering Causal Additive Noise Models
Additive noise models are commonly used to infer the causal direction fo...
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04/29/2020
Batch Normalization in Quantized Networks
Implementation of quantized neural networks on computing hardware leads ...
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11/28/2019
Qini-based Uplift Regression
Uplift models provide a solution to the problem of isolating the marketi...
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09/30/2019
Random Bias Initialization Improving Binary Neural Network Training
Edge intelligence especially binary neural network (BNN) has attracted c...
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09/26/2019
Smart Ternary Quantization
Neural network models are resource hungry. Low bit quantization such as ...
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09/18/2019
A Study on Binary Neural Networks Initialization
Initialization plays a crucial role in training neural models. Binary Ne...
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09/10/2019
Differentiable Mask Pruning for Neural Networks
Pruning of neural networks is one of the well-known and promising model ...
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05/24/2019
Parallel Coordinate Order for High-Dimensional Data
Visualization of high-dimensional data is counter-intuitive using conven...
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02/05/2019
Active Learning for High-Dimensional Binary Features
Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater dev...
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01/30/2019
Uplift Regression: The R Package tools4uplift
Uplift modeling aims at predicting the causal effect of an action such a...
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01/28/2019
Activation Adaptation in Neural Networks
Many neural network architectures rely on the choice of the activation f...
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01/18/2019
Foothill: A Quasiconvex Regularization Function
Deep neural networks (DNNs) have demonstrated success for many supervise...
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12/31/2018
BNN+: Improved Binary Network Training
Deep neural networks (DNN) are widely used in many applications. However...
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09/23/2018
Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models
The inference of the causal relationship between a pair of observed vari...
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12/07/2017