Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling

Uncertainty estimation for machine learning models is of high importance in many scenarios such as constructing the confidence intervals for model predictions and detection of out-of-distribution or adversarially generated points. In this work, we show that modifying the sampling distributions for dropout layers in neural networks improves the quality of uncertainty estimation. Our main idea consists of two main steps: computing data-driven correlations between neurons and generating samples, which include maximally diverse neurons. In a series of experiments on simulated and real-world data, we demonstrate that the diversification via determinantal point processes-based sampling achieves state-of-the-art results in uncertainty estimation for regression and classification tasks. An important feature of our approach is that it does not require any modification to the models or training procedures, allowing straightforward application to any deep learning model with dropout layers

Structured Light for Second-Harmonic Spectroscopy in Mie-Resonant AlGaAs Nanoparticles

We employ doughnut-shaped cylindrical vector beams to observe the enhanced second-harmonic generation from individual subwavelength AlGaAs nanoparticles which support both electric and magnetic multipolar Mie-type resonances at the fundamental and double frequencies

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions. First, we show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. Importantly, the proposed approach allows to disentangle explicitly aleatoric and epistemic uncertainties. The resulting method works directly in the feature space. However, one can apply it to any neural network by considering an embedding of the data induced by the network. We demonstrate the strong performance of the method in uncertainty estimation tasks on text classification problems and a variety of real-world image datasets, such as MNIST, SVHN, CIFAR-100 and several versions of ImageNet.Comment: NeurIPS 2022 pape

LM-Polygraph: Uncertainty Estimation for Language Models

Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often "hallucinate", i.e., fabricate facts without providing users an apparent means to discern the veracity of their statements. Uncertainty estimation (UE) methods are one path to safer, more responsible, and more effective use of LLMs. However, to date, research on UE methods for LLMs has been focused primarily on theoretical rather than engineering contributions. In this work, we tackle this issue by introducing LM-Polygraph, a framework with implementations of a battery of state-of-the-art UE methods for LLMs in text generation tasks, with unified program interfaces in Python. Additionally, it introduces an extendable benchmark for consistent evaluation of UE techniques by researchers, and a demo web application that enriches the standard chat dialog with confidence scores, empowering end-users to discern unreliable responses. LM-Polygraph is compatible with the most recent LLMs, including BLOOMz, LLaMA-2, ChatGPT, and GPT-4, and is designed to support future releases of similarly-styled LMs.Comment: Accepted at EMNLP-202

Enhanced Second-Harmonic Generation with Structured Light in AlGaAs Nanoparticles Governed by Magnetic Response

We employ structured light for the second-harmonic generation from subwavelength AlGaAs nanoparticles that support both electric and magnetic multipolar Mie resonances. The vectorial structure of the pump beam allows addressing selectively Mie-resonant modes and control the strength of the generated nonlinear fields. We observe experimentally the enhancement of the second-harmonic generation for the azimuthally polarized vector beams near the magnetic dipole resonance, and match our observations with the numerical decomposition of the Mie-type multipoles for the fundamental and generated second-harmonic fields.The authors thank Prof. B. Luther-Davies for support. E. Melik-Gaykazyan acknowledges the support of the Student Mobility Scholarship of the President of the Russian Federation, the Russian Ministry of Education and Science (no. 14.W03.31.0008), and the Russian Foundation for Basic Research (project no. 18-32-01038). A. Fedyanin acknowledges the support of the Russian Foundation of Basic Research (project no. 18-29-20097) and the Quantum Technology Center, Moscow State University. Nonlinear simulations were supported by the Russian Science Foundation (project no. 18-72-10140). K. Koshelev acknowledges the support of the Foundation for the Advancement of Theoretical Physics and Mathematics BASIS (Russia)

Bloch Surface Wave Photonic Device Fabricated by Femtosecond Laser Polymerisation Technique

We applied femtosecond laser polymerisation technique to fabricate a novel Bloch surface wave integrated photonic device with a compact coupling scheme. The device consisted of a waveguide, coupling and decoupling gratings and focusing and defocusing triangles. We manufactured an array of devices with varying geometrical parameters of waveguide. Excitation and propagation of Bloch surface wave waveguide modes were studied by direct and back focal plane imaging. The obtained results prove that the maskless and flexible femtosecond laser polymerisation technique may be applied for fabrication of Bloch-surface-wave based integrated photonics

Tailoring Third-Harmonic Diffraction Efficiency by Hybrid Modes in High-Q Metasurfaces

Metasurfaces are versatile tools for manipulating light; however, they have received little attention as devices for the efficient control of nonlinearly diffracted light. Here, we demonstrate nonlinear wavefront control through third-harmonic generation (THG) beaming into diffraction orders with efficiency tuned by excitation of hybrid Mie-quasi-bound states in the continuum (BIC) modes in a silicon metasurface. Simultaneous excitation of the high-Q collective Mie-type modes and quasi-BIC modes leads to their hybridization and results in a local electric field redistribution. We probe the hybrid mode by measuring far-field patterns of THG and observe the strong switching between (0,-1) and (-1,0) THG diffraction orders from 1:6 for off-resonant excitation to 129:1 for the hybrid mode excitation, showing tremendous contrast in controlling the nonlinear diffraction patterns. Our results pave the way to the realization of metasurfaces for novel light sources, telecommunications, and quantum photonics