RNNs learn device models from input/output data.
problem Learning complex device models from limited data.
method Empirical study using RNNs to model six different devices.
result RNNs can generate functional software-only models of hardware devices.
Paper evaluates security of deep learning from side-channel attacks.
problem Security of deep learning algorithms from side-channel attacks.
method Uses hardware performance counters to test for information leakage in CNNs.
result Shows alarms for potential data privacy breaches in CNNs.
This paper uses machine learning to assist automation engineers in decision making.
problem Imperfect decision making by automation engineers leads to multiple iterations and increased time for software development.
method Defined challenges and proposed solutions using machine learning for automation engineering, including code classification, finding similar code snippets, and hardware selection.
result Paragraph embedding techniques achieved an F1-score of 72% for classifying automation using code snippets, and autoencoder models for hardware recommendation achieved p@3 and p@5 of 0.79 and 0.95, respectively.
Paper extends method to detect reverse engineering attacks on DNN classifiers.
problem Detecting reverse engineering attacks on deep neural network image classifiers.
method Extend ADA approach to detect RE attacks (ADA-RE).
result Successfully detects 'stealthy' RE attacks before they launch test-time evasion attacks.
Adds layers to NNs to protect them from reverse engineering.
problem Extracting the underlying model of a Neural Network.
method Introducing parasitic layers that approximate a noisy identity mapping with a Convolutional NN.
result The protected NN's predictions remain mostly unchanged while making reverse-engineering more complex.
We introduce a general procedure called `reverse engineering' that can be used to construct infinite families of smooth 4-manifolds in a given homeomorphism type. As one of the applications of this technique, we produce an infinite family of pairwise nondiffeomorphic 4-manifolds homeomorphic to CP^2#3(-CP^2).
We introduce Dimple, a fully open-source API for probabilistic modeling. Dimple allows the user to specify probabilistic models in the form of graphical models, Bayesian networks, or factor graphs, and performs inference (by automatically deriving an inference engine from a variety of algorithms) on the model. Dimple a…
Researchers can reverse-engineer deep ReLU networks from outputs.
problem Recovering a deep ReLU network from its outputs.
method Dissecting region boundaries to identify neuron states and weights.
result Weights and neuron arrangement of a deep ReLU network can be recovered.
Framework learns to optimize tensor programs for various hardware.
problem Manual optimization of tensor operators for deep learning limits applicability and increases engineering costs.
method Learning-based statistical cost models guide tensor operator implementations over billions of variants.
result Framework delivers performance competitive with hand-tuned libraries across multiple hardware targets.
Design automation optimizes deep learning models for various hardware.
problem Designing efficient deep learning models requires balancing algorithm and hardware.
method Proposes design automation techniques for specialized neural networks, including auto pruning and quantization.
result Learning-based automation achieves superior performance and efficiency compared to human design.
Reverse engineered RNNs reveal line attractor dynamics for sentiment classification.
problem Understanding how recurrent neural networks solve sequential tasks like sentiment classification.
method Dynamical systems analysis to reverse engineer trained RNNs, identifying fixed points and linearized dynamics.
result Trained RNNs converge to low-dimensional line attractor dynamics, providing interpretable solutions.
Method extracts original shapes from CAD models using clustering and implicitization.
problem Reverse engineering of geometric models from numerical representations.
method Combining clustering analysis with approximate implicitization.
result Automatic recovery of algebraic hypersurfaces of any degree in any dimension.
IReEn reveals functionality of black-box agents via iterative neural synthesis.
problem Revealing the functionality of a black-box agent without privileged information.
method Iterative refinement of candidate programs using neural program synthesis.
result The approach finds a functional equivalent program in 78% of cases, outperforming state-of-the-art.
Using virtual stock markets with artificial interacting software investors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the act…
Quantum machine learns faster by reverse annealing on AQCs.
problem Training RBMs on AQCs is hard due to low qubit connectivity.
method Embedding RBM nodes to virtual qubits, semantic quantum search, reverse annealing schedule.
result Reverse annealing accelerates RBM training and improves reconstruction scores.
Neural model predicts procedure names in stripped binaries.
problem Reverse engineering stripped executables with limited debug information.
method Combines static analysis with neural models to predict procedure names.
result Improves prediction accuracy by 28% and 100% over state-of-the-art models.
MLPerf benchmarks ML inference systems across diverse hardware.
problem Challenges in evaluating ML inference systems due to variety and complexity.
method MLPerf prescribes a set of rules and best practices for comparability.
result First call for submissions yielded over 600 reproducible measurements.
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with…
Engine SixtyFour uses neural networks to play Crazyhouse chess.
problem Developing a neural network-based evaluation function for Crazyhouse chess.
method Created an ensemble model for Crazyhouse chess using a neural network.
result Early versions of the network have a playing level comparable to a strong amateur.
New method improves convergence of gradient descent for non-convex, non-reversible Markov chains.
problem Improving convergence of gradient descent for non-convex, non-reversible Markov chains.
method Introducing a new technique that varies the mixing levels of the Markov chains to establish non-ergodic convergence under wider step sizes.
result Established non-ergodic convergence for non-convex problems and non-reversible finite-state Markov chains.
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks…
As an application of `reverse engineering' technique introduced by R. Fintushel, D. Park and R. Stern \cite{FPS}, we construct an infinite family of fake (2n+2l-1)CP^2#(2n+4l-1)(-CP^2)'s for all n \ge 0, l \ge 1.
Survey of ART neural networks for engineering applications.
problem Understanding and utilizing ART neural networks for various machine learning tasks.
method Comprehensive review of classic and modern ART models, describing learning dynamics and engineering properties.
result Compilation of ART models and their properties for engineering applications.
Locally private reinforcement learning protects individual environments from reverse engineering.
problem Protecting private information in distributed reinforcement learning environments.
method Locally differentially private algorithms that protect local agents' models from adversarial reverse engineering.
result Demonstrated that the proposed algorithm performs well under local differential privacy (LDP).
Deep neural network improves ultrasound imaging quality.
problem Rapid performance degradation of traditional beamformers in poor data acquisition conditions.
method Developed a deep learning-based beamformer.
result Significant performance gain in contrast-to-noise ratio and structural similarity for focused and planar imaging.
BoTorch optimizes Bayesian optimization with MC methods and auto-differentiation.
problem Efficient global optimization for various applications.
method Monte-Carlo acquisition functions, sample average approximation, auto-differentiation, variance reduction.
result Improved sample efficiency compared to other libraries.
Paper reviews neuromorphic engineering features and compares analog vs digital systems.
problem Lack of consensus and unclear features in neuromorphic engineering.
method Review of recent work, comparison of machine learning accelerator chips.
result Analog processing and reduced bit precision architectures offer best efficiencies.
A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of ne…
Unified understanding of neural networks on group operations verified.
problem Understanding and verifying neural networks trained on group operations.
method Investigated one-hidden-layer neural networks trained on binary operation of finite groups, revealing structure and providing a compact proof of model performance.
result Verified explanation applies to a large fraction of networks trained on the symmetric group S5, providing a >=95% accuracy bound for 45% of models.
We perform the first study of the tradeoff space of access methods and replication to support statistical analytics using first-order methods executed in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical analytics systems differ from conventional SQL-analytics in the amount and types of memory …
MLCapsule protects sensitive data by executing models locally.
problem Protecting sensitive data and model security in MLaaS.
method Local execution of models on client side, coupled with security measures.
result Models executed locally without data leaving the client, protecting against attacks.
To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult …
Advocates for user-friendly RL problem descriptions to improve usability and generalization.
problem Usability and generalization challenges in RL for non-engineers.
method Development of user-friendly description languages for RL problems.
result Improved ability of RL algorithms to generalize to new problems.
We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock sc…
Machine learning infers time-reversible dynamics from data.
problem Learn time-reversible dynamics constrained by initial and final conditions.
method Machine learning algorithms solve boundary value problems for deterministic and stochastic dynamics.
result Inferred time-reversible dynamics for various types of systems.
New method identifies drivers from car logs without reverse-engineering CAN protocol.
problem Identifying drivers from in-vehicle network logs without access to exact signal semantics.
method Machine learning techniques applied to off-the-shelf data.
result Driver re-identification accuracy of 75-85% on a dataset of 33 drivers.
Investigates ways to train larger models with fewer resources, finding that test loss depends only on the actual number of trainable parameters.
problem Training larger models for cheaper under hardware constraints.
method Emulates an increase in effective parameters using frozen random parameters or fast structured transforms.
result Scaling laws cannot be deceived by spurious parameters; test loss depends only on the actual number of trainable parameters.
The paper optimizes neural network inference on mobile GPUs.
problem Limited computing power and thermal constraints on mobile CPUs.
method Leverage mobile GPUs for neural network inference.
result Real-time inference of deep neural networks on Android and iOS devices.
CodeX improves DNN acceleration on FPGAs by encoding and customizing bitwidth.
problem Efficiently accelerating deep neural networks on FPGAs with limited memory.
method Nonlinear encoding, automated bitwidth customization, FPGA streaming buffers.
result Average 4.65x throughput improvement on MNIST, SVHN, CIFAR-10.
Developing active inference agents for edge devices with limited resources.
problem Creating effective active inference agents on edge devices with limited computational resources.
method Introducing a software toolbox to accelerate the development of active inference agents by non-experts.
result Accelerates the democratization of active inference agents for edge devices.
Minimal complexes for two-strand braids defined directly.
problem Determining minimal complexes for braids.
method Explicit formulas derived from educated guesswork and reverse engineering.
result Construction of minimal complexes homotopy equivalent to Rickard complexes.
New neuromorphic hardware learns MNIST digits efficiently.
problem Limited scalability and in-hardware learning in existing neuromorphic hardware.
method Low-cost scalable NoC-based SNN architecture with in-hardware STDP learning.
result Demonstrated learning capability of the hardware architecture.
Morph accelerates 3D CNNs for video recognition, reducing energy consumption and improving performance.
problem Efficiently accelerating 3D CNNs for video recognition is challenging due to their large memory footprint and higher dimensionality.
method Designing a flexible accelerator called Morph that adapts to different spatial and temporal tiling strategies, and codesigning a software infrastructure to control the hardware.
result Morph achieves up to 3.4x reduction in energy consumption and up to 5.1x improvement in performance/watt compared to a baseline 3D CNN accelerator.
Stealthy hardware Trojan exploits DLA architecture vulnerabilities.
problem Security of DLA deployed on hardware accelerators.
method Input Interception Attack (IIA) exploiting statistical properties of DLA outputs.
result Stealthy Trojan can trigger with some definiteness.
Ansor generates high-performance tensor programs for deep learning.
problem Generating high-performance tensor programs for deep learning on various hardware platforms is challenging.
method Ansor uses a hierarchical representation of the search space, sampling programs, and evolutionary search with a learned cost model to find high-performance programs.
result Ansor improves deep neural network execution performance up to 3.8x on Intel CPU, 2.6x on ARM CPU, and 1.7x on NVIDIA GPU.
DeepFense detects adversarial inputs in real-time for DL models.
problem Malicious attacks on autonomous DL systems in critical applications.
method Formalizes adversarial detection as an optimization problem, trains modular redundancies for validation.
result Achieves up to two orders of magnitude performance improvement with online adversarial sample detection.
New KWS neural networks improve accuracy and power efficiency.
problem Maximizing accuracy and power efficiency in KWS systems.
method Hardware Aware Training (HAT) for LMU-based neural networks.
result Achieved state-of-the-art accuracy and low parameter counts.
PINNs help construct minimal surfaces in hyperbolic space, proving a conjecture about HOMFLY polynomial coefficients.
problem Constructing minimal surfaces in hyperbolic space asymptotic to a given knot.
method Physics-informed neural networks (PINNs) with careful engineering of model architecture and residual evaluation.
result Numerical evidence supports Joel Fine's conjecture about minimal surfaces and HOMFLY polynomial coefficients.