We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …
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48 results for “fault-tolerance”
Enhances fault tolerance of neural networks for security-critical applications.
problem Fault tolerance of neural networks is biased and can lead to severe consequences in security-critical scenarios.
method Proposes a revised implementation that significantly enhances the fault tolerance property of neural networks with detailed mathematical analysis.
result Significantly increased fault tolerance of neural networks for security-critical applications.
Fault-tolerant neural networks inspired by biological error correction codes.
problem Achieving reliable computation with unreliable neurons.
method Using biological error correction codes from grid cells in the mammalian cortex to develop a fault-tolerant neural network.
result Noisy biological neurons operate below a fault-tolerance threshold, suggesting a mechanism for reliable computation in the brain.
Paper addresses fault-tolerance in distributed machine learning with stochastic gradient descent.
problem Fault-tolerance in distributed stochastic gradient descent (D-SGD) for machine learning.
method Proposes norm-based comparative gradient elimination (CGE) to robustify D-SGD against Byzantine faulty agents.
result CGE guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions.
This paper reviews quantum machine learning from NISQ to fault tolerance.
problem The challenges and opportunities in quantum machine learning.
method Comprehensive review of quantum machine learning concepts.
result Coverage of NISQ and fault-tolerant quantum computing approaches.
New model evaluates how well models handle input faults.
problem Fault tolerance of models to input variations.
method Evaluates fault tolerance using information-based characteristic for arbitrary valid inputs.
result Proposes a new way to measure model robustness.
Paper introduces deep neural decoders for near-term fault-tolerant quantum experiments.
problem Efficient decoders for quantum error correction under realistic noise.
method Deep neural decoders complemented by traditional algorithms.
result Deep neural decoders perform well in low noise regimes.
New framework reduces fault tolerance costs in machine learning.
problem Fault tolerance in iterative-convergent machine learning algorithms.
method Developed a general framework to quantify and design strategies for checkpoint-based fault tolerance.
result SCAR reduces iteration cost of partial failures by 78% - 95%.
The paper examines how few neuron failures affect neural network performance.
problem Understanding the impact of random neuron failures on neural network output.
method Probabilistic analysis of neural networks with small random crashes using Taylor expansion in the continuous limit.
result Provable bounds on error in neural network output under small random crashes.
Enhances fault tolerance in neural networks with a novel multi-criteria objective function.
problem Faults in neural networks, especially in deep learning accelerators, reduce classification accuracy.
method Modelled as two networks: Feature Extractor and Classifier. Proposed a novel multi-criteria objective function combining unsupervised and supervised training.
result The proposed approach achieves high accuracy with superior fault tolerance compared to existing regularizers.
Paper proposes FTT-NAS to create fault-tolerant CNNs for edge devices.
problem Faults in edge devices affect deep learning applications.
method Formalized fault models, implemented FTT-NAS, incorporated FTT.
result Discovered CNNs outperform baseline architectures with fault tolerance.
SignSGD improves distributed learning by tolerating faulty devices, including Byzantine adversaries.
problem Faulty devices, including Byzantine adversaries, disrupt convergence in distributed learning.
method Developed SignSGD from SGD, proving its robustness to Byzantine adversaries with upper bound convergence rate.
result SignSGD converges in the presence of Byzantine adversaries, providing a fault-tolerant solution.
Paper tackles fault tolerance in distributed linear regression.
problem Fault tolerance in distributed linear regression with Byzantine faulty agents.
method Robustified distributed gradient descent using norm-based filters.
result Server can determine linear relationship deterministically in a log-linear computation cost.
GKP codes connect quantum gates to algebraic curves, enabling fault-tolerant quantum computation.
problem Implementing fault-tolerant quantum computation in quantum harmonic oscillator systems.
method Exploring the topological and algebraic structure of GKP codes, showing how gates correspond to symplectic automorphisms and mapping class groups of surfaces.
result GKP Clifford gates are identified with symplectic automorphisms of GKP lattices and mapping class groups of surfaces, providing a topological interpretation of fault tolerance.
TonY simplifies distributed ML job management.
problem Managing distributed ML jobs is complex and resource-intensive.
method TonY is an open-source orchestrator for distributed ML jobs.
result TonY simplifies distributed ML job management.
Paper proposes methods to train RBF networks under faults.
problem Training RBF networks with fault tolerance.
method Two novel ADMM-based algorithms using MCP and l0-norm.
result Both methods globally converge to a unique limit point.
Zeno improves SGD for distributed learning with faulty nodes.
problem Fault tolerance for distributed SGD with arbitrary faulty workers.
method Suspicion-based fault-tolerance mechanism with ranking-based preference.
result Proved convergence of SGD for non-convex problems under faulty scenarios.
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
problem Real-time price prediction for high-volatility cryptocurrencies.
method Spark-based architecture, sentiment analysis, online learning.
result Real-time adaptation of learning algorithms to new data.
Ray is a distributed system for AI applications that learn from continuous interactions.
problem Demanding systems requirements for next-gen AI applications.
method Unified interface for task-parallel and actor-based computations, distributed scheduler, fault-tolerant store.
result Demonstrated scaling beyond 1.8 million tasks per second and better performance for reinforcement learning.
New fault-tolerant quantum gates for homological LDPC codes with constant or almost-constant rate.
problem Fault-tolerant quantum computing for homological LDPC codes with constant or almost-constant encoding rate.
method Derive generic formula for transversal and logical gates acting on 3-manifolds, using higher symmetries and cup product cohomology.
result Parallelizable logical gates for homological LDPC codes with constant or almost-constant rate.
Framework fuses RGB images and depth maps for self-driving car control.
problem Fault tolerance in self-driving cars with sensor failures.
method Deep neural network architecture for sensor fusion.
result Framework can learn to use relevant sensor information even when one fails.
New protocols implement logical gates on encoded qubits with minimal overhead.
problem Efficiently performing universal logical gates on encoded qubits with minimal overhead.
method Using topological codes associated to hyperbolic surfaces, we introduce protocols to implement Dehn twists through constant depth unitary circuits.
result Demonstrated the possibility of applying universal logical gate sets on encoded qubits through constant depth unitary circuits and with constant space overhead.
Meta-reinforcement learning improves fault-adaptive control efficiency.
problem Adaptive control under abrupt system faults with strict time constraints.
method Model-agnostic meta learning (MAML) with a fault library of prior policies.
result Improved sample efficiency and quick adaptation to new faults.
ByRDiE algorithm handles Byzantine failures in decentralized learning.
problem Byzantine failures in distributed learning.
method Byzantine-resilient distributed coordinate descent (ByRDiE) algorithm.
result ByRDiE enables high-dimensional distributed learning in the presence of Byzantine failures.
Quantum computers outperform classical methods in density modeling.
problem Density modeling with quantum computers.
method Quantum-classical separation for density modeling.
result Quantum computers offer a super-polynomial advantage over classical algorithms for density modeling.
Quantum advantage in derivative pricing requires 8k qubits and 54M T-depth.
problem Quantum advantage in pricing derivatives.
method Re-parameterization method combining pre-trained variational circuits and fault-tolerant quantum computing.
result Benchmark use cases require 8k logical qubits and a T-depth of 54 million.
DeepDyve uses simpler neural networks to verify DNNs for faults.
problem Faults in DNNs, especially those not crafted by attackers.
method Pre-trained, simpler neural networks for dynamic verification.
result Reduces 90% of risks with 10% overhead.
RODMAN improves ML-based disk failure prediction accuracy in cloud environments.
problem Imperfect data quality in real-world cloud environments degrades ML-based disk failure prediction accuracy.
method RODMAN uses three data preprocessing techniques: failure-type filtering, spline-based data filling, and automated pre-failure backtracking.
result RODMAN significantly improves prediction accuracy compared to no preprocessing.
Study on topological order on fractal geometries, proving no-go theorem and fault-tolerant gates.
problem Investigating topological order on fractal geometries embedded in n dimensions.
method Using quantum error-correcting codes and systolic geometry to diagnose topological order.
result Proves no-go theorem for topological order on 2D fractals, survival on higher dimensions, and construction of fault-tolerant gates.
Fault-tolerant federated learning for non-uniform data.
problem Faulty workers corrupting data in federated learning.
method Fault-resilient proximal gradient (FRPG) algorithm with Nesterov's acceleration and local FRPG for reduced communication.
result FRPG and LFRPG converge faster than robust stochastic aggregation.
Quantum models are rephrased as kernel methods, improving performance.
problem Improving quantum machine learning models by encoding data into quantum states.
method Rephrasing quantum models as kernel methods and using support vector machines.
result Kernel-based training finds better quantum models than variational circuit training.
Phocas improves robustness in SGD against Byzantine failures.
problem Byzantine failures in distributed SGD.
method Proposes a novel aggregation rule for Byzantine-resilient SGD.
result Proves Byzantine resilience of the proposed aggregation rules.
Quantum computing poses a threat to Bitcoin and Ethereum, but only to spending and not mining.
problem Quantum computing threat to Bitcoin and Ethereum
method Separation of Shor's and Grover's quantum algorithms
result Quantum algorithms' impact on Bitcoin and Ethereum
Mitigates faults in DNNs by clipping activation values, improving their resilience.
problem Fault tolerance of DNNs in safety-critical applications.
method Clipping activation functions to reduce impact of faulty weights.
result Significant improvement in classification accuracy (68.92%) for fault mitigation.
Quantum GAN improves volatility modeling in finance.
problem Improving volatility modeling in finance using GANs.
method Developed a quantum GAN for volatility modeling.
result Quantum GAN provides exponential advantage over classical methods.
New attacks break robust aggregation methods for SGD in Byzantine-tolerant systems.
problem Breaking Byzantine-tolerant techniques in distributed machine learning.
method Inner product manipulation to break robust aggregation methods (median and Krum).
result Coordinate-wise median and Krum can be broken using new attack strategies.
Paper proposes a secure protocol for federated learning.
problem Combining robustness, privacy, and security in federated learning.
method Secure two-server protocol for federated learning.
result Offers both input privacy and Byzantine-robustness.
Cost-sensitive learning improves predictive maintenance by reducing costs.
problem Predictive maintenance models often lead to higher costs than expected.
method Propose selecting models based on economic costs rather than traditional metrics.
result Cost reduction and fault-tolerant policies achieved through cost-sensitive learning.
Federated learning challenges are addressed with multi-task learning and MOCHA method.
problem Statistical and systems challenges in federated learning.
method MOCHA, a novel systems-aware optimization method.
result MOCHA achieves significant speedups in federated learning.
Zeno++ improves robustness of asynchronous SGD in fully asynchronous settings.
problem Byzantine failures in fully asynchronous SGD.
method Estimates descent of loss after applying candidate gradient.
result Proves convergence for non-convex problems under Byzantine failures.
New algorithm improves decentralized learning in the presence of Byzantine faults.
problem Byzantine faults in decentralized learning on arbitrary graphs.
method Proposes ClippedGossip for Byzantine-robust consensus and optimization.
result First to provably converge to a specified neighborhood of the stationary point for non-convex objectives.
GaDei optimizes deep learning training-as-a-service by reducing communication traffic.
problem High communication traffic in deep learning training-as-a-service.
method GaDei is an optimized shared-memory scale-up parameter server design.
result GaDei significantly outperforms state-of-the-art parameter-server based implementations.
Topological theory for qLDPC codes enables non-Clifford gates and magic state injection.
problem Fault-tolerant quantum computation in qLDPC codes with non-Clifford gates and magic state resources.
method Developed a topological theory using simplicial or CW complex structures and deformation retraction.
result Achieved non-Clifford gates and magic state injection in qLDPC codes with constant rate and polynomial distance.
Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better per…
Mango automates hyperparameter tuning for large-scale ML training.
problem Manual hyperparameter tuning is tedious and inefficient for large-scale machine learning.
method Parallel hyperparameter tuning with intelligent search strategies and flexible abstractions.
result Mango achieves comparable performance to Hyperopt while supporting distributed computing.
In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propo…
DynBRO learns robustly from dynamic Byzantine workers.
problem Fault-tolerant distributed learning with dynamic Byzantine workers.
method Multi-level Monte Carlo (MLMC) gradient estimation and adaptive learning rate.
result DynaBRO nearly matches static setting's convergence rate with Byzantine worker changes.
Variational Quantum Algorithmsquant-ph
VQAs use classical optimization to train quantum circuits, promising quantum advantage.
problem High computational cost of quantum simulations and solving large-scale problems.
method Variational Quantum Algorithms (VQAs) use classical optimizers to train parametrized quantum circuits.
result VQAs are a promising strategy for obtaining quantum advantage.