Search Results for author:
Found 25 papers, 6 papers with code
Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects
Then we estimate the velocity of the moving objects using the estimated LiDAR velocity and the Doppler velocity of moving objects obtained by clustering.
BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components.
Extreme Multi-label Learning for Semantic Matching in Product Search
In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts.
Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees
In industrial applications, these models operate at extreme scales, where every bit of performance is critical.
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
Explore the Potential of CNN Low Bit Training
Through this dynamic precision framework, we can reduce the bit-width of convolution, which is the most computational cost, while keeping the training process close to the full precision floating-point training.
PECOS: Prediction for Enormous and Correlated Output Spaces
In this paper, we propose the Prediction for Enormous and Correlated Output Spaces (PECOS) framework, a versatile and modular machine learning framework for solving prediction problems for very large output spaces, and apply it to the eXtreme Multilabel Ranking (XMR) problem: given an input instance, find and rank the most relevant items from an enormous but fixed and finite output space.
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$.
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100 s overhead.
Provable Non-linear Inductive Matrix Completion
Inductive matrix completion (IMC) method is a standard approach for this problem where the given query as well as the items are embedded in a common low-dimensional space.
Taming Pretrained Transformers for Extreme Multi-label Text Classification
However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.
Extreme Multi-Label Classification
General Classification
+3
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
We consider a generalization of mixed regression where the response is an additive combination of several mixture components.
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Complex performance measures, beyond the popular measure of accuracy, are increasingly being used in the context of binary classification.
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks
A standard approach to modeling this problem is Inductive Matrix Completion where the predicted rating is modeled as an inner product of the user and the item features projected onto a latent space.
Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels
In this paper, we consider parameter recovery for non-overlapping convolutional neural networks (CNNs) with multiple kernels.
Recovery Guarantees for One-hidden-layer Neural Networks
For activation functions that are also smooth, we show $\mathit{local~linear~convergence}$ guarantees of gradient descent under a resampling rule.
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
In this work, we show that, by decomposing training of Structural Support Vector Machine (SVM) into a series of multiclass SVM problems connected through messages, one can replace expensive structured oracle with Factorwise Maximization Oracle (FMO) that allows efficient implementation of complexity sublinear to the factor domain.
Mixed Linear Regression with Multiple Components
Furthermore, our empirical results indicate that even with random initialization, our approach converges to the global optima in linear time, providing speed-up of up to two orders of magnitude.
Coordinate-wise Power Method
The vanilla power method simultaneously updates all the coordinates of the iterate, which is essential for its convergence analysis.
Online Classification with Complex Metrics
The proposed framework is general, as it applies to both batch and online learning, and to both linear and non-linear models.
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
In this work, we show that a margin-maximizing loss with l1 penalty, in case of Extreme Classification, yields extremely sparse solution both in primal and in dual without sacrificing the expressive power of predictor.
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
Over the past decades, Linear Programming (LP) has been widely used in different areas and considered as one of the mature technologies in numerical optimization.
Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
Given a symmetric nonnegative matrix $A$, symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$, usually with much fewer columns than $A$, such that $A \approx HH^T$.
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
We consider the class of optimization problems arising from computationally intensive L1-regularized M-estimators, where the function or gradient values are very expensive to compute.
