124 research outputs found
A Comprehensive Survey on Knowledge Distillation of Diffusion Models
Diffusion Models (DMs), also referred to as score-based diffusion models,
utilize neural networks to specify score functions. Unlike most other
probabilistic models, DMs directly model the score functions, which makes them
more flexible to parametrize and potentially highly expressive for
probabilistic modeling. DMs can learn fine-grained knowledge, i.e., marginal
score functions, of the underlying distribution. Therefore, a crucial research
direction is to explore how to distill the knowledge of DMs and fully utilize
their potential. Our objective is to provide a comprehensible overview of the
modern approaches for distilling DMs, starting with an introduction to DMs and
a discussion of the challenges involved in distilling them into neural vector
fields. We also provide an overview of the existing works on distilling DMs
into both stochastic and deterministic implicit generators. Finally, we review
the accelerated diffusion sampling algorithms as a training-free method for
distillation. Our tutorial is intended for individuals with a basic
understanding of generative models who wish to apply DM's distillation or
embark on a research project in this field
Enhancing Adversarial Robustness via Score-Based Optimization
Adversarial attacks have the potential to mislead deep neural network
classifiers by introducing slight perturbations. Developing algorithms that can
mitigate the effects of these attacks is crucial for ensuring the safe use of
artificial intelligence. Recent studies have suggested that score-based
diffusion models are effective in adversarial defenses. However, existing
diffusion-based defenses rely on the sequential simulation of the reversed
stochastic differential equations of diffusion models, which are
computationally inefficient and yield suboptimal results. In this paper, we
introduce a novel adversarial defense scheme named ScoreOpt, which optimizes
adversarial samples at test-time, towards original clean data in the direction
guided by score-based priors. We conduct comprehensive experiments on multiple
datasets, including CIFAR10, CIFAR100 and ImageNet. Our experimental results
demonstrate that our approach outperforms existing adversarial defenses in
terms of both robustness performance and inference speed
Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness
Adversarial attacks can mislead neural network classifiers. The defense
against adversarial attacks is important for AI safety. Adversarial
purification is a family of approaches that defend adversarial attacks with
suitable pre-processing. Diffusion models have been shown to be effective for
adversarial purification. Despite their success, many aspects of diffusion
purification still remain unexplored. In this paper, we investigate and improve
upon three limiting designs of diffusion purification: the use of an improved
diffusion model, advanced numerical simulation techniques, and optimal control
of randomness. Based on our findings, we propose Purify++, a new diffusion
purification algorithm that is now the state-of-the-art purification method
against several adversarial attacks. Our work presents a systematic exploration
of the limits of diffusion purification methods
Mining the Relationship between Emoji Usage Patterns and Personality
Emojis have been widely used in textual communications as a new way to convey
nonverbal cues. An interesting observation is the various emoji usage patterns
among different users. In this paper, we investigate the correlation between
user personality traits and their emoji usage patterns, particularly on overall
amounts and specific preferences. To achieve this goal, we build a large
Twitter dataset which includes 352,245 users and over 1.13 billion tweets
associated with calculated personality traits and emoji usage patterns. Our
correlation and emoji prediction results provide insights into the power of
diverse personalities that lead to varies emoji usage patterns as well as its
potential in emoji recommendation tasks.Comment: To appear at The International AAAI Conference on Web and Social
Media (ICWSM) 201
Enhanced superconductivity at the interface of W/SrRuO point contact
Differential resistance measurements are conducted for point contacts (PCs)
between tungsten tip approaching along the axis direction and the
plane of SrRuO single crystal. Three key features are found.
Firstly, within 0.2 mV there is a dome like conductance enhancement due to
Andreev reflection at the normal-superconducting interface. By pushing the W
tip further, the conductance enhancement increases from 3\% to more than 20\%,
much larger than that was previously reported, probably due to the pressure
exerted by the tip. Secondly, there are also superconducting like features at
bias higher than 0.2 mV which persists up to 6.2 K, resembling the enhanced
superconductivity under uniaxial pressure for bulk SrRuO crystals
but more pronounced here. Third, the logarithmic background can be fitted with
the Altshuler-Aronov theory of tunneling into quasi two dimensional electron
system, consistent with the highly anisotropic electronic system in
SrRuO.Comment: prb style, 9 pages, 8 fig
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
Medication for neurological diseases such as the Parkinson's disease usually
happens remotely away from hospitals. Such out-of-lab environments pose
challenges in collecting timely and accurate health status data. Individual
differences in behavioral signals collected from wearable sensors also lead to
difficulties in adopting current general machine learning analysis pipelines.
To address these challenges, we present a method for predicting the medication
status of Parkinson's disease patients using the public mPower dataset, which
contains 62,182 remote multi-modal test records collected on smartphones from
487 patients. The proposed method shows promising results in predicting three
medication statuses objectively: Before Medication (AUC=0.95), After Medication
(AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical
records with the attention weights learned through a Transformer model. Our
method provides an innovative way for personalized remote health sensing in a
timely and objective fashion which could benefit a broad range of similar
applications.Comment: Accepted to ICDH-2023. Camera ready with supplementary materia
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