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Found 17 papers, 8 papers with code
Knowledge-Augmented Methods for Natural Language Processing
Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models.
On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations
Namely, we show that when a small number of cells (e. g. 1%-5% of cells) have their positions shifted such that a measure of global congestion is guaranteed to remain unaffected (e. g. 1% of the design adversarially shifted by 0. 001% of the layout space results in a predicted decrease in congestion of up to 90%, while no change in congestion is implied by the perturbation).
Monolithic Silicon-Photonics Linear-Algebra Accelerators Enabling Next-Gen Massive MIMO
A system-on-chip (SoC) photonic-electronic linear-algebra accelerator with the features of wavelength-division-multiplexing (WDM) based broadband photodetections and high-dimensional matrix-inversion operations fabricated in advanced monolithic silicon-photonics (M-SiPh) semiconductor process technology is proposed to achieve substantial leaps in computation density and energy efficiency, including realistic considerations of energy/area overhead due to electronic/photonic on-chip conversions, integrations, and calibrations through holistic co-design methodologies to support linear-detection based massive multiple-input multiple-output (MIMO) decoding technology requiring the inversion of channel matrices and other emergent applications limited by linear-algebra computation capacities.
ChatGPT at the Speed of Light: Optical Comb-Based Monolithic Photonic-Electronic Linear-Algebra Accelerators
This paper proposes to adopt advanced monolithic silicon-photonics integrated-circuits manufacturing capabilities to achieve a system-on-chip photonic-electronic linear-algebra accelerator with the features of optical comb-based broadband incoherent photo-detections and high-dimensional operations of consecutive matrix-matrix multiplications to enable substantial leaps in computation density and energy efficiency, with practical considerations of power/area overhead due to photonic-electronic on-chip conversions, integrations, and calibrations through holistic co-design approaches to support attention-head mechanism based deep-learning neural networks used in Large Language Models and other emergent applications.
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design
Many of the works use inconsistent experimental settings and there are no comprehensive studies on the effect of FL-specific experimental variables on the results and practical insights for a more comparable and consistent FL experimental setup.
When Do Curricula Work in Federated Learning?
Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL.
Neural Routing in Meta Learning
Inspired by the widely recognized finding in neuroscience that distinct parts of the brain are highly specialized for different types of tasks, we aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks.
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises.
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants.
NeuCASL: From Logic Design to System Simulation of Neuromorphic Engines
Neuromorphic computing is a promising such approach with its brain-inspired circuitry, use of emerging technologies, and low-power nature.
LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)
It is due to the highly compute and memory-intensive nature of the training phase.
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server.
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
Each neuron in the first layer directly maps to an interpretable if-then rule after training, and the output neuron in the second layer directly maps to a disjunction of the first-layer rules to form the decision rule set.
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation.
Differentially-private Federated Neural Architecture Search
To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties.
