DLC enhances distillation-based continual learning with lightweight plugins.
problem Stability-plasticity dilemma in distillation-based continual learning.
method DLC deploys lightweight residual plugins into the classifier-proximal layer of a shared feature extractor.
result Significant 8% accuracy gain on large-scale benchmarks with minimal parameter increase.
VarDeepPCA: A Sampling-Free Variational DNN Plugin for OOD Segmentation with Uncertainty Estimation
problem Deep neural networks (DNNs) fail to generalize to out-of-distribution (OOD) medical images due to variations in scanners and acquisition protocols.
method VarDeepPCA is a lightweight variational DNN framework that learns a distribution of valid anatomical geometries using small in-distribution datasets.
result VarDeepPCA restores segmentation maps produced by existing methods on OOD data to improve anatomical plausibility and reduce errors.
The paper optimizes estimating transport maps between distributions.
problem Estimating optimal transport maps between distributions.
method Plugin approach using optimal couplings and extensions.
result Minimax optimality of the proposed estimators.
This report works out the details of a closed-form, fully Bayesian, multiclass, openset, generative pattern classifier using multivariate Gaussian likelihoods, with conjugate priors. The generative model has a common within-class covariance, which is proportional to the between-class covariance in the conjugate prior. …
OpenML-Python API simplifies access to OpenML for Python users.
problem Limited access to OpenML for Python users.
method Developed a Python API (OpenML-Python) to integrate OpenML with Python-based tools.
result Facilitates easy access to OpenML's datasets, tasks, and experiments.
We consider statistical estimation of superhedging prices using historical stock returns in a frictionless market with d traded assets. We introduce a plugin estimator based on empirical measures and show it is consistent but lacks suitable robustness. To address this we propose novel estimators which use a larger set …
VarDeepPCA refines medical image segmentation from small datasets, improving anatomical plausibility and reducing errors.
problem Medical image segmentation fails on out-of-distribution data due to variations in scanners and protocols.
method VarDeepPCA learns valid anatomical geometries using only small in-distribution datasets, providing uncertainty estimates.
result VarDeepPCA restores segmentation maps to OOD data, improving anatomical plausibility and reducing errors.
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on perfor…
A new framework for lightweight BNNs learns heteroscedastic uncertainties efficiently.
problem Learning heteroscedastic uncertainties from BNNs for lightweight networks.
method Embedding heteroscedastic variances into BNN parameters and using moment propagation for inference.
result Improves predictive performance for lightweight BNNs without increasing parameter count.
DBQ quantizes lightweight networks efficiently for resource-constrained devices.
problem High computational and storage complexity of deep neural networks on resource-constrained devices.
method A differentiable non-uniform quantizer that can be mapped onto efficient ternary-based dot product engines.
result Achieves state-of-the-art results with minimal training overhead and best accuracy-complexity trade-off.
RSAC improves lightweight continuous learning efficiency.
problem Excessive training time and memory usage in continuous learning.
method Regularized subspace approximation classifier with feature reduction and regularization.
result RSAC achieves more efficient continuous learning than prior methods.
Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse…
iSTFTNet2 improves iSTFTNet's speed and lightness with 1D-2D CNN.
problem Efficiently synthesizing high-fidelity speech.
method Improved iSTFTNet using 1D-2D CNNs for temporal and spectrogram structures.
result iSTFTNet2 is faster and more lightweight with comparable speech quality.
DeepLine automates ML pipeline generation using reinforcement learning.
problem Automatic generation of end-to-end ML pipelines combining multiple algorithms.
method Deep Reinforcement Learning with hierarchical actions filtering.
result DeepLine outperforms state-of-the-art approaches in accuracy and computational cost.
Augment small datasets with synthetic backgrounds to train lightweight CNNs for human pose estimation.
problem Training CNNs from limited real-world data for human pose estimation.
method Synthetic background substitution for data augmentation.
result Improves generalization to unseen environments.
CMoS improves time series forecasting with minimal parameters.
problem Efficiently forecasting time series data with limited resources.
method CMoS directly models chunk-wise spatial correlations, using Correlation Mixing and Periodicity Injection techniques.
result CMoS outperforms state-of-the-art models with minimal parameters.
Federated learning framework improves model generalization and privacy.
problem Communication overhead and statistical heterogeneity in FL.
method Prototypes and lightweight adapters for local model refinement.
result Improves classification accuracy over baseline algorithms.
Novel stability bounds for OT maps improve density estimation.
problem Estimating optimal transport maps between probability distributions.
method Developed novel stability bounds for OT maps, reducing the problem to density estimation.
result Stability bounds allow for sharper guarantees without smoothness assumptions.
Mapper-GIN simplifies 3D point cloud classification with lightweight structure.
problem Robust 3D point cloud classification under corruption.
method Mapper algorithm for structural decomposition, GIN for graph classification.
result Mapper-GIN achieves competitive accuracy with minimal parameters.
Paper presents a lightweight, unobtrusive method to protect edge device data privacy.
problem Protecting inference data privacy in IoT edge devices with limited compute power.
method A lightweight neural network at edge devices to obfuscate inference data without indicating obfuscation.
result Effectively protects inference data confidentiality while preserving backend accuracy.
GIBLy adds geometric priors to 3D segmentation models, improving performance with minimal overhead.
problem Lack of explicit geometric information in 3D semantic segmentation models.
method Introduces GIBLy, a lightweight geometric inductive bias layer that integrates learnable geometric priors into existing 3D segmentation pipelines.
result Consistent performance gains across multiple benchmarks, including up to +11.5% mIoU on TS40K with PTV3.
BERTino is a lightweight Italian DistilBERT model for NLP tasks.
problem High computational and memory demands of large language models.
method Developed a DistilBERT model tailored for Italian language.
result F1 scores comparable to BERTBASE with significant speed improvements.
In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segmentation. …
TinyXRA assesses financial risks from 10-K reports using a lightweight transformer model.
problem Comprehensive risk assessment from financial reports, distinguishing between upside and downside risk.
method Lightweight transformer model with dynamic attention, incorporating skewness, kurtosis, and Sortino ratio.
result State-of-the-art predictive accuracy and transparent risk assessments.
We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a…
Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative appro…
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve c…
Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score. Analyzing these prediction scores is useful to understand the classifier behavior and to assess its reliability. We present an interactive visualization that facilitates per-class an…
A lightweight framework improves convergence and stability of PINNs for complex PDEs.
problem Training instability and reduced accuracy in PINNs for complex PDEs.
method Adaptive curvature correction using secant information to optimize first-order optimizers.
result Consistent improvements in convergence speed, stability, and accuracy over standard optimizers.
Optimizes neural architecture search to generate novel lightweight models.
problem Over-reliance on expert knowledge limits NAS to local optima, preventing architectural breakthroughs.
method Casts NAS as an optimization problem, introduces a hierarchical graph-based search space, and uses Bayesian optimization.
result Generates extremely lightweight yet competitive models on six benchmark datasets.
Optimal first-order methods are shown to be fundamental limits in functional estimation.
problem Optimal functional estimation under weak conditions.
method Formalization of functional estimation with black-box nuisance function estimates and derivation of minimax lower bounds.
result First-order methods are optimal under weak conditions, but higher-order methods can outperform them when nuisance function structure is known.
ELF improves FM forecasts by efficiently using online feedback.
problem Fixed FMs fail to adapt to current data characteristics.
method ELF-Forecaster learns current data distribution, ELF-Weighter combines forecasts.
result ELF improves FM forecasts across various datasets.
We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows us to change selected attributes while preserving other information. Our method, matrix subspace projection, is much simpler than previous ap…
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
problem Accurate forecasting of urban air pollution for public health and policy guidance.
method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.
result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high R2 values. Kolmogorov-Arnold network improves GW catalog posterior construction.
problem Efficiently constructing posterior distributions for GW catalogs.
method Using the Kolmogorov-Arnold network to create lightweight neural density estimators.
result Kolmogorov-Arnold network achieves superior interpretability and accuracy in posterior construction.
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physical properties of the real world, such as continuity, multi-modal partially-observable states with…
ReRe detects anomalies in real-time for time series data.
problem Real-time anomaly detection for time series data requires human intervention or domain knowledge and high computation complexity.
method ReRe uses two lightweight LSTM models to predict and determine anomalies based on historical data and adaptive thresholds.
result ReRe detects anomalies in real-time without requiring human intervention or domain knowledge.
Paper discusses privacy issues in IoT and proposes a lightweight neural network approach.
problem Privacy concerns in IoT due to extensive data collection and processing.
method Developed a privacy-preserving inference approach for IoT objects and a deep neural network in the cloud.
result Satisfactory performance of the proposed approach on the MNIST dataset.
A new method for growing neural networks by splitting neurons, improving efficiency.
problem Optimizing neural network structures, especially for lightweight architectures.
method A progressive training approach using steepest descent to adaptively grow and split neurons.
result The method provides a computationally efficient way to optimize neural network structures.
Method transfers feature representation from large to small models using perception coherence.
problem Transfer feature representation from large to small models.
method Defines perception coherence, proposes loss function to minimize.
result Method outperforms or achieves on-par performance compared to strong baseline methods.
FLAML automates model selection and hyperparameter tuning with low resource cost.
problem Automating model selection and hyperparameter tuning for ad-hoc datasets and metrics.
method Conducts trials of different configurations on training data, optimizing for low computational cost.
result Significantly outperforms top-ranked AutoML libraries under smaller budget constraints.
FEDS distills LIC model knowledge into a lightweight student for efficient compression.
problem Efficiently compress images with high performance and low resources.
method FEDS combines feature alignment and entropy-based loss for lightweight compression.
result Student model matches teacher's performance while reducing parameters and speeding up.
iSTFTNet speeds up mel-spectrogram vocoders without sacrificing quality.
problem Efficiently converting mel-spectrograms to speech with minimal computation.
method Replaces convolutional layers with iSTFT after frequency dimension reduction.
result Significant reduction in computational cost with comparable quality.
Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, {\em can we use QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest that simply furt…
SWIFT improves time series forecasting on edge devices with wavelet decomposition.
problem Efficient time series forecasting on resource-constrained devices.
method Wavelet decomposition, cross-band fusion, and shared linear mapping.
result SWIFT achieves state-of-the-art performance on multiple datasets.
This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.
problem Inference delays and energy inefficiency in energy-harvesting devices.
method Developed a power trace-aware and exit-guided network compression algorithm for multi-exit neural networks.
result Superior accuracy and reduced latency compared to state-of-the-art techniques.
This study improves estimation of the first principal component in multivariate functional data.
problem Estimating the first principal component of multivariate random processes.
method Defined covariance functions and operators, introduced LASSO optimization, and established minimax lower bounds.
result The method provides an optimal variance in the minimax sense for estimating eigenelements.
Improved UAV navigation and landing using deep learning.
problem Autonomous navigation and landing of UAVs with high accuracy.
method Multimodal fusion of visual and inertial sensor data using deep neural networks.
result 25% improvement in pose estimation accuracy compared to traditional methods.