Efficiently attacks black-box image classifiers using biased sampling.
problem Efficiently attacking black-box image classifiers without access to confidence scores.
method Reinterpreting Boundary Attacks as a biased sampling framework.
result Combination of biases outperforms state of the art by a wide margin.
Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google's milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their cloud platform and as of very recently has opened them up further to allow…
Large-scale datasets have played a significant role in progress of neural network and deep learning areas. YouTube-8M is such a benchmark dataset for general multi-label video classification. It was created from over 7 million YouTube videos (450,000 hours of video) and includes video labels from a vocabulary of 4716 c…
Research aims to predict fallen angel bonds' bankruptcy using machine learning.
problem Predicting which fallen angel bonds will become investment grade or go bankrupt.
method Used four classification methods (logistic regression, KNN, SVM, NN) and Google Cloud's automated machine learning.
result Google Cloud's machine learning model performed best in over-sampled and feature selection data sets.
Machine vision-guided 3D medical image compression improves segmentation accuracy.
problem High data traffic and computation costs in cloud-based medical image analysis.
method Developed a machine vision-oriented 3D image compression framework for medical segmentation.
result Significantly higher segmentation accuracy at the same compression rate or better compression rate under the same accuracy.
A tailored HTR system improves CER to 0.015 for medieval Latin.
problem Digitizing handwritten medieval Latin records for a low-resource language.
method End-to-end pipeline using image segmentation and transformer-based models with extensive data augmentation.
result Best-performing setup achieved CER of 0.015, superior to commercial models.
This paper compares machine learning models for pricing European options.
problem Pricing European options using traditional methods like Black Scholes Model.
method Google AutoML Regressor, TensorFlow Neural Networks, and XGBoost Gradient Boosting Decision Trees.
result All models outperformed the Black Scholes Model in terms of mean absolute error.
We present a solution to "Google Cloud and YouTube-8M Video Understanding Challenge" that ranked 5th place. The proposed model is an ensemble of three model families, two frame level and one video level. The training was performed on augmented dataset, with cross validation.
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full …
Note that this paper is superceded by "Black-Box Adversarial Attacks with Limited Queries and Information." Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods -…
A new adversarial attack method using structured search and contextual bandits.
problem Black-box adversarial attacks on deep learning models.
method Structured search space and Bayesian optimization for contextual bandits.
result Achieves state-of-the-art success rates and query efficiencies.
New method for black-box adversarial attacks using pretrained model embeddings.
problem Efficiently attacking unknown target networks with high-level semantic patterns.
method Learn a low-dimensional embedding using a pretrained model, then search within the embedding space.
result Significant reduction in the number of queries for black-box adversarial attacks.
The concept of a Point Cloud has played an increasingly important role in many areas of Engineering, Science, and Mathematics. Examples are: LIDAR, 3D-Printing, Data Analysis, Computer Graphics, Machine Learning, Mathematical Visualization, Numerical Analysis, and Monte Carlo Methods. Entering point cloud into Google r…
Efficient black-box evasion attacks against deep networks with fewer queries.
problem Optimizing targeted evasion attacks with limited query budget.
method Formalized problem, evaluated benefits of using substitute models, presented two new attack strategies.
result Attack strategies are as effective as previous techniques but require significantly fewer queries.
Simple method for black-box adversarial attacks with low query efficiency.
problem Efficient construction of adversarial images in the black-box setting.
method Randomly sampling from an orthonormal basis and adjusting images.
result Demonstrated unprecedented query efficiency in both untargeted and targeted attacks.
NP-Attack reduces query counts for black-box adversarial attacks.
problem Efficiency of black-box adversarial attacks is low.
method Uses Neural Process to characterize image structure and find adversarial examples.
result NP-Attack significantly decreases query counts.
Graph Neural Networks improve 3D object detection in LiDAR point clouds.
problem Challenges in processing LiDAR data due to its 3D geometry and massive volume.
method Proposes a Graph Neural Network (GNN) based framework for 3D object detection.
result GNNs successfully identify objects in 3D LiDAR point clouds.
Predicts and classifies computational jobs for efficient resource allocation in cloud centers.
problem Efficiently scheduling and assigning resources to computational jobs in cloud centers.
method Applied LSTM neural network for job arrival prediction and BIRCH clustering for job classification.
result Improved accuracy in predicting and classifying computational jobs compared to existing methods.
FTL improves model training across data silos but requires optimization.
problem Data privacy and security concerns in federated learning.
method Quantitative analysis of FATE on Google Cloud, identifying bottlenecks.
result Inter-process communication, data encryption, and networking condition are major performance issues.
3D point cloud attacks examine how neural networks can be fooled.
problem Understanding how 3D neural networks can be exploited by attackers.
method Examined two categories of attacks: distributional and shape attacks.
result Some shape attacks can fool 3D point cloud classification models even after preprocessing.
Framework for efficient defect classification and inspection.
problem Adaptive defect classification and inspection from high volume data.
method Continual learning framework for dynamic classifier updates.
result Efficient storage and computational needs reduction.
Point cloud is the most fundamental representation of 3D geometric objects. Analyzing and processing point cloud surfaces is important in computer graphics and computer vision. However, most of the existing algorithms for surface analysis require connectivity information. Therefore, it is desirable to develop a mesh st…
The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices has given the computer vision community 3D information that conventional RGB cameras cannot provide. This data is often stored as a point cloud. In this paper, we present a novel method to apply the concept of convolutional neural networks to th…
VALAN is a framework for navigation agents in photo-realistic environments.
problem Developing agents for indoor navigation tasks.
method Deep reinforcement learning with SEED RL architecture.
result VALAN framework can solve a variety of RL problems.
Paper proposes a hierarchical approach to malware detection in cloud environments.
problem Malware threat in cloud computing environments.
method Machine learning on graphs, hypergraphs, and natural language for malware detection and analysis.
result Federated learning for malware detection in multicloud environments.
Voxel-FPN detects 3D objects from point clouds using raw LIDAR data.
problem 3D object detection in point cloud data for autonomous driving.
method Bottom-up voxel extraction and top-down feature fusion.
result Voxel-FPN outperforms baselines on the KITTI-3D benchmark.
Modern vision-based reinforcement learning techniques often use convolutional neural networks (CNN) as universal function approximators to choose which action to take for a given visual input. Until recently, CNNs have been treated like black-box functions, but this mindset is especially dangerous when used for control…
Enhanced 3D shape analysis using information geometry.
problem Challenges in comparing 3D point clouds due to their unstructured nature and complex geometry.
method Information geometric framework for 3D point cloud shape analysis using Gaussian Mixture Models (GMMs) on a statistical manifold. Proposed MSKL divergence with upper and lower bounds.
result MSKL provides stable and monotonically varying values that directly reflect geometric variation, outperforming traditional distances and existing KL approximations.
Proposes a robust 3D classification method for sparse point clouds.
problem Invariance to rotation, positional shift, scaling, and robustness to point sparsity in point cloud classification.
method Introduces a graph-based feature learning approach with an end-to-end neural network.
result Significantly improves 3D object classification and retrieval tasks with sparse point clouds.
Improved CNN accuracy for encrypted data using approximate activation functions.
problem Low accuracy in classifying encrypted data using homomorphic encryption.
method Used a fourth-order polynomial approximation of the Swish activation function with batch normalization for homomorphic encryption.
result Achieved 99.22% accuracy on MNIST and 80.48% on CIFAR-10, improving by 0.04% and 4.11% respectively.
Method trains vision and control policies on real robots quickly.
problem Training vision-based control policies on real robots efficiently.
method Multi-task Reinforcement Learning with auxiliary tasks.
result Significant learning speed-ups and task learning from-scratch.
Optimized neural networks for Edge TPU achieve high accuracy in real-time image classification.
problem Designing neural networks for hardware accelerators to achieve optimal performance.
method Hardware-aware neural architecture search and model customization for Edge TPU.
result Improved accuracy-latency tradeoff on Pixel 4's Edge TPU compared to existing models.
Efficient search methods can outperform random search on challenging tasks.
problem Comparing the performance of efficient and random search methods in neural architecture search.
method Comparison of weight sharing and random search methods on progressively larger search spaces for image classification and detection.
result Efficient search methods can provide substantial gains over random search on large, realistic tasks.
Hierarchical Foresight improves robot vision tasks by planning long-term goals.
problem Compounding uncertainty and scalability issues in long horizon video prediction.
method Subgoal generation and planning using hierarchical visual foresight (HVF).
result Achieves nearly 200% performance improvement in vision-based manipulation tasks.
In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de …
Survey on deep neural networks for vision and speech.
problem Improving intelligent vision and speech systems.
method Review of deep learning models and challenges.
result Emerging technologies show promise for future systems.
This paper optimizes deep learning training by efficiently sharding weight updates across replicas.
problem Redundant weight update computation on all replicas in data-parallel training.
method Automatic sharding of weight updates using static analysis and transformations on the training graph.
result Substantial speedups achieved on large-scale models using Cloud TPUs.
Bitcoin's attention is linked to Google Trends data, not general uncertainty.
problem Bitcoin's correlation with Google Trends data was previously misunderstood.
method Analyzed bidirectional relationships between Bitcoin returns and Google Trends attention over six days.
result Information flows from Bitcoin volatility to Google Trends attention, not the other way.
Google Trends data improves economic forecasts of private consumption.
problem Improving economic forecasts of private consumption.
method Machine learning techniques applied to categorized Google search data.
result Google data can identify patterns to generate a leading indicator in real time.
Paper introduces TVaRD, a new topological risk measure for financial portfolios.
problem Traditional risk measures like VaR and CVaR are insufficient for complex market conditions.
method Topological data analysis (TDA) using cohomology groups on financial time series data.
result TVaRD reveals significant changes in financial time series during stress conditions.
A new method for efficient optimal partial transport in 1D.
problem Limitation of equal mass assumption in optimal transport.
method Sliced Optimal Partial Transport (Sliced-OPT) algorithm.
result Sliced-OPT demonstrates computational and accuracy benefits.
Study matches two noisy point clouds with geometric transformations and relabeling.
problem Matching two noisy point clouds with orthogonal transformations and relabeling.
method Information-theoretic results and Ping-Pong algorithm for computational alignment.
result The Ping-Pong algorithm retrieves the planted signal after one step.
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…
Google Trends can lead to misleading forecasts if not used carefully.
problem Misleading forecasts due to the variability of Google Trends data.
method Analyzing the variability of Google Trends data and proposing solutions.
result Google Trends can be a problem if not used with caution.
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research direction…
Benchmarking TPU, GPU, and CPU for deep learning models.
problem Improving performance in deep learning training.
method ParaDnn benchmark suite for FC, CNN, and RNN models on TPU, GPU, and CPU.
result TPU, GPU, and CPU have unique strengths for different types of models.
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable metho…
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistica…