Search Results for author:
Found 21 papers, 4 papers with code
ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis
We identify that on the same task, inference time across models can differ by 2. 7x-136. 3x, while the median inter-packet waiting time is often 6-8 orders of magnitude higher than the inference time!
DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning
The end-to-end pipeline achieves a top-5 classification accuracy of 0. 80.
EdgeServe: A Streaming System for Decentralized Model Serving
The relevant features for a machine learning task may arrive as one or more continuous streams of data.
VizExtract: Automatic Relation Extraction from Data Visualizations
Visual graphics, such as plots, charts, and figures, are widely used to communicate statistical conclusions.
CIAO: An Optimization Framework for Client-Assisted Data Loading
In this paper, we present CIAO, a tunable system to enable client cooperation with the server to enable efficient partial loading and data skipping for a given workload.
Databases
Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios
The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum.
Analysis of Random Perturbations for Robust Convolutional Neural Networks
Recent work has extensively shown that randomized perturbations of neural networks can improve robustness to adversarial attacks.
Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning
As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue.
Band-limited Training and Inference for Convolutional Neural Networks
The convolutional layers are core building blocks of neural network architectures.
Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT
This approach delivers an accuracy close to 100% compared to auto-correlation (AC) and energy detection (ED) approaches.
A Perturbation Analysis of Input Transformations for Adversarial Attacks
The existence of adversarial examples, or intentional mis-predictions constructed from small changes to correctly predicted examples, is one of the most significant challenges in neural network research today.
Deep Unsupervised Cardinality Estimation
To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.
AlphaClean: Automatic Generation of Data Cleaning Pipelines
The analyst effort in data cleaning is gradually shifting away from the design of hand-written scripts to building and tuning complex pipelines of automated data cleaning libraries.
Databases
Learning to Optimize Join Queries With Deep Reinforcement Learning
Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space.
Databases
Parametrized Hierarchical Procedures for Neural Programming
Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise.
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet.
Robotics
Multi-Level Discovery of Deep Options
Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces.
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of-date, or outlier data.
Communication-Efficient Distributed Dual Coordinate Ascent
Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning.
