Researchers successfully implemented quantum autoencoders using quantum adders in a cloud quantum computer.
problem Reducing resource usage in quantum computations.
method Experimental implementation of quantum autoencoders using approximate quantum adders in a cloud quantum computer.
result Experimental fidelities are in good agreement with theoretical predictions, proving the feasibility of quantum autoencoders via quantum adders.
GeneDisco benchmarks experimental design for drug discovery.
problem Vast experimental design space in drug discovery.
method Machine learning for optimal experimental design.
result Standardised benchmark suite for active learning.
System interprets complex treatment effects for personalized policies.
problem Complex, hard-to-understand treatment effect models.
method Scalable, interpretable personalized experimentation system.
result Learned explanations and generated interpretable policies.
The paper optimizes spatial experimental designs to improve causal effect estimation.
problem Optimizing spatial experimental designs to enhance causal effect estimation accuracy.
method Proposes a surrogate function for MSE and uses graph cut algorithms to learn optimal designs.
result The method accommodates spatial interference and covariance, is computationally efficient, and validated by theoretical and numerical experiments.
We verify CNNs using reachability analysis and transformations.
problem Verifying neural-based perception systems implemented by CNNs.
method Reachability analysis for feed-forward neural networks with MILP encodings.
result The notion of local robustness cannot be captured by previous robustness notions.
NeLLoC improves image compression with parallel decoding.
problem Image compression with OOD generalization.
method Local autoregressive model with parallel decoding.
result Significant gains in compression runtime.
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning based work ESE suffers from degradation of performance/energy efficiency due to the irregular network structure after pruning. We propose bl…
We proposed a new statistical dependency measure called Copula Dependency Coefficient(CDC) for two sets of variables based on copula. It is robust to outliers, easy to implement, powerful and appropriate to high-dimensional variables. These properties are important in many applications. Experimental results show that C…
Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid …
New method discovers discrepancies between simplified models and experimental data.
problem Model discrepancies in nonlinear systems lead to significant deviations from true behavior.
method Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to discover sparse model terms.
result Improvement in performance with a discrepancy model in simulations.
Deep learning for smile detection on embedded systems.
problem Real-time smile detection in low-resource environments.
method Comparison of neural network architectures on NVidia Jetson platform with asynchronous multithreading.
result Low complexity architectures can achieve similar performance to larger networks with less computation.
New method improves robustness of Bayesian experimental design.
problem Bayesian experimental design's sensitivity to prior distribution changes.
method Introduces robust expected information gain (REIG) and uses KL-divergence ambiguity sets.
result REIG stabilizes sampling-based EIG estimation and compensates for prior variability.
GPdoemd optimizes experiments for model discrimination using Gaussian processes.
problem Discriminating between competing models when data is limited.
method Developed a new design criterion and Gaussian process surrogate method for black-box models.
result Demonstrated improved model discrimination using Gaussian process surrogates.
Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis', can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting re…
Develops a flexible batched experimentation framework for limited adaptivity.
problem Challenges of continual reallocation in bandit algorithms with delayed feedback.
method Computational framework leveraging Gaussian sequential experiment and dynamic programming.
result Improves statistical power over standard methods, even compared to Bayesian bandit algorithms.
FEAT estimates free energy using adaptive transports.
problem Estimating free energy across scientific domains.
method Uses learned transports and stochastic interpolants.
result Provides consistent, minimum-variance estimators.
We provide the first experimental results on non-synthetic datasets for the quasi-diagonal Riemannian gradient descents for neural networks introduced in [Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a previously unpublished electroencephalogram dataset. The quasi-diagonal Riemannian alg…
Novel parallel GNN predicts protein-ligand interactions with high accuracy.
problem Accurate prediction of protein-ligand interactions for drug design.
method Parallel Graph Neural Networks (GNN) integrating 3D structural data.
result GNN achieves high accuracy in predicting binary interactions and activity.
The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach t…
Adapts deep learning models trained on simulated images for use with real images.
problem Difficulty in training deep neural networks on large amounts of experimental data.
method Adversarial domain adaptation method to mitigate domain shift between simulated and experimental image data.
result Adversarial domain adaptation successfully mitigates domain shift and improves numerical observer performance.
Researchers compute large quantum invariants for 3-manifolds.
problem Computing large values of Turaev-Viro invariants for 3-manifolds.
method Optimized backtracking algorithm, lattice point counting, preprocessing strategy, multi-precision arithmetics.
result Experimentally verified improvements over state-of-the-art implementations, supporting volume conjecture.
Paper improves speech separation by using deep neural networks for more accurate density priors.
problem Improving the accuracy of source priors for independent vector analysis in speech separation.
method Estimating the derivative of speech density using deep neural networks to optimize performance indices.
result Neural network density priors outperform previous ones in convergence speed and SIR.
Orthogonium offers unified, efficient layers for robust deep learning.
problem Fragmented and computationally demanding implementations of orthogonal and 1-Lipschitz layers.
method Unified, efficient PyTorch library providing orthogonal and 1-Lipschitz layers.
result Reduced overhead and standardized tools for robust experimentation.
Speed bumps reduce but do not fully eliminate investment in fast trading technology.
problem Limiting low-latency trading to curb investment in fast trading technology.
method Built an experimental trading platform to test the effects of speed bumps on investment in fast trading technology.
result Asymmetric speed bumps reduce investment in speed by only 20%, and increasing the magnitude further reduces investment by 8.33%. Symmetric speed bumps have no effect on investment levels.
Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guara…
Improves A/B testing by detecting minor treatment effects.
problem Challenges in identifying small average treatment effects.
method Maximum probability-driven two-armed bandit (TAB) process with weighted mean volatility statistic.
result Significant improvement in A/B testing with reduced experimental costs.
The paper deals with the adaptation of a new measure for the unsupervised feature selection problems. The proposed measure is based on space filling concept and is called the coverage measure. This measure was used for judging the quality of an experimental space filling design. In the present work, the coverage measur…
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constit…
The paper develops scalable Bayesian models for dynamic covariance matrices using Gaussian processes.
problem Modeling dynamic and heteroskedastic covariance matrices for multivariate time series.
method Gradient-based variational inference for Wishart and inverse Wishart processes, with modifications for scalability and factoring.
result The modified models can scale to high-dimensional covariance matrices and outperform multivariate GARCH in covariance forecasting.
Research uses machine learning to find central nodes and cliques in YouTube social networks.
problem Identifying central nodes and cliques in YouTube social networks.
method Unsupervised machine learning, Python programming, Bron-Kerbosch algorithm.
result Successfully found central nodes through clique-centrality and degree centrality.
Polynomial-time methods count and sample DAGs from equivalence classes.
problem Counting and sampling DAGs from Markov equivalence classes.
method Polynomial-time algorithms for DAGs.
result Counting and sampling can be done in polynomial time.
Efficiently designs experiments without integrating posterior distributions.
problem Computational inefficiency in Bayesian experimental design for PDE-based models.
method Likelihood-free approach using ANN to approximate conditional expectation.
result Significant reduction in observation model evaluations.
A new method for stochastic optimization using virtual gradients.
problem Stochastic optimization challenges in computational efficiency and memory usage.
method Inspired by dynamic programming, SVGD uses a computational graph and automatic differentiation for efficient optimization.
result Experimental results show SVGD outperforms other methods on multiple datasets and network models.
New q-neurons improve neural network performance.
problem Improving neural network activation functions.
method Introducing q-neurons based on Jackson's q-derivatives with stochastic parameters. result Consistently improved performance over state-of-the-art activation functions.
Transformers can adaptively select and perform various machine learning tasks in context.
problem Transformers' ability to perform multiple machine learning tasks without explicit prompting.
method Statistical theory and efficient implementation of in-context gradient descent, enabling transformers to implement various algorithms and tasks.
result Transformers can implement a broad class of standard machine learning algorithms in context, including algorithm selection.
Dataset of Bose-Einstein condensates images aids ML in many-body physics.
problem Understanding solitons in Bose-Einstein condensates.
method Machine learning (ML) framework with convolutional neural networks and physics-informed classifiers.
result Automatic labeling of solitonic excitations in experimental images.
Platform combines RL and language models to study narrative influence on AI decisions.
problem Understanding how narrative elements shape AI decision-making.
method Dual-system architecture with reinforcement learning and language model integration.
result Initial experiments show narrative frameworks can influence AI decision-making.
The paper provides a method to minimize regret in estimate-then-optimize decision-making.
problem Errors in estimation lead to sub-optimal decisions in data-driven decision-making.
method A novel bound on regret for smooth and unconstrained optimization problems, followed by experimental design to minimize this regret.
result A general procedure for experimental design to minimize regret resulting from estimate-then-optimize.
We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and becaus…
Paper presents a data-driven method for option pricing.
problem Option pricing accuracy under market volatility.
method Data-driven ensemble approach based on no-arbitrage theory.
result Model performance validated with real data.
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to in…
We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previo…
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Existing work uses value-based methods and the usual primitive action setting. In this paper, we propose robust methods for learning temporally …
FANN-on-MCU enables efficient neural network inference on IoT devices.
problem Energy-efficient neural network inference on resource-constrained IoT devices.
method Open-source toolkit for ARM Cortex-M and RISC-V microcontrollers.
result Efficient neural network execution with low latency and power consumption.
The paper tackles intervention generalization using factor graph models.
problem Generalizing from past experiments to novel conditions is hard due to combinatorial space of possible interventions.
method The approach is based on intervention factor models (IFMs) and factor graph models.
result Conditions for identifiability of expected outcomes of new regimes are derived.
This paper introduces Non-Autonomous Input-Output Stable Network(NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and al…
Quantum computing speeds up risk analysis by efficiently sampling copulas.
problem Efficiently modeling tail dependence and risk measures in financial risk analysis.
method Quantum computing implementation of copula models for risk aggregation.
result The MB11 copula family is suitable for capturing tail dependence structures in risk factors.