Search Results for author:
Found 28 papers, 8 papers with code
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
After blocks are adjusted for current test domain, we generate pseudo-labels by averaging given test images and corresponding flipped counterparts.
INSTA-BEEER: Explicit Error Estimation and Refinement for Fast and Accurate Unseen Object Instance Segmentation
Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation.
Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation
This approach differs from conventional knowledge distillation frameworks, which use the L_p distance metrics and offer the advantage of converging well when reducing the distance between features of different resolutions.
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
The real-time operation of large-scale infrastructure networks requires scalable optimization capabilities.
Block Selection Method for Using Feature Norm in Out-of-distribution Detection
In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images.
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems
This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting.
Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs.
Stochastic Optimization
Optimization and Control
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN
Understanding assembly instruction has the potential to enhance the robot s task planning ability and enables advanced robotic applications.
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive.
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks
In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ).
S-SGD: Symmetrical Stochastic Gradient Descent with Weight Noise Injection for Reaching Flat Minima
The proposed method is compared with the conventional SGD method and previous weight-noise injection algorithms using convolutional neural networks for image classification.
Multiple Classification with Split Learning
We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning.
A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl
We present a general graph-based modeling abstraction for optimization that we call an OptiGraph.
Optimization and Control
Quantized Neural Networks: Characterization and Holistic Optimization
This study proposes a holistic approach for the optimization of QDNNs, which contains QDNN training methods as well as quantization-friendly architecture design.
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs).
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems.
On the Convergence of the Dynamic Inner PCA Algorithm
In this work, we show that this algorithm is a specialized variant of a coordinate maximization algorithm.
SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of Low-Precision Deep Neural Networks
Model averaging is a promising approach for achieving the good generalization capability of DNNs, especially when the loss surface for training contains many sharp minima.
Knowledge distillation for optimization of quantized deep neural networks
Knowledge distillation (KD) is a very popular method for model size reduction.
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices
The RNN implementation on embedded devices can suffer from excessive DRAM accesses because the parameter size of a neural network usually exceeds that of the cache memory and the parameters are used only once for each time step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Fixed-point optimization of deep neural networks with adaptive step size retraining
Fixed-point optimization of deep neural networks plays an important role in hardware based design and low-power implementations.
Quantized neural network design under weight capacity constraint
The complexity of deep neural network algorithms for hardware implementation can be lowered either by scaling the number of units or reducing the word-length of weights.
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA.
Generative Knowledge Transfer for Neural Language Models
In this paper, we propose a generative knowledge transfer technique that trains an RNN based language model (student network) using text and output probabilities generated from a previously trained RNN (teacher network).
Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks
Gesture recognition is a very essential technology for many wearable devices.
Fixed-Point Performance Analysis of Recurrent Neural Networks
Recurrent neural networks have shown excellent performance in many applications, however they require increased complexity in hardware or software based implementations.
Resiliency of Deep Neural Networks under Quantization
In this work, the effects of retraining are analyzed for a feedforward deep neural network (FFDNN) and a convolutional neural network (CNN).
