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2305.12755
|
2023-05-22T06:26:05Z
|
GNCformer Enhanced Self-attention for Automatic Speech Recognition
|
[
"J. Li",
"Z. Duan",
"S. Li",
"X. Yu",
"G. Yang"
] |
In this paper,an Enhanced Self-Attention (ESA) mechanism has been put forward
for robust feature extraction.The proposed ESA is integrated with the recursive
gated convolution and self-attention mechanism.In particular, the former is
used to capture multi-order feature interaction and the latter is for global
feature extraction.In addition, the location of interest that is suitable for
inserting the ESA is also worth being explored.In this paper, the ESA is
embedded into the encoder layer of the Transformer network for automatic speech
recognition (ASR) tasks, and this newly proposed model is named GNCformer. The
effectiveness of the GNCformer has been validated using two datasets, that are
Aishell-1 and HKUST.Experimental results show that, compared with the
Transformer network,0.8%CER,and 1.2%CER improvement for these two mentioned
datasets, respectively, can be achieved.It is worth mentioning that only 1.4M
additional parameters have been involved in our proposed GNCformer.
|
[
"cs.SD",
"cs.CL",
"eess.AS"
] | false |
2305.12798
|
2023-05-22T07:52:04Z
|
LM-Switch: Lightweight Language Model Conditioning in Word Embedding
Space
|
[
"Chi Han",
"Jialiang Xu",
"Manling Li",
"Yi Fung",
"Chenkai Sun",
"Nan Jiang",
"Tarek Abdelzaher",
"Heng Ji"
] |
In recent years, large language models (LMs) have achieved remarkable
progress across various natural language processing tasks. As pre-training and
fine-tuning are costly and might negatively impact model performance, it is
desired to efficiently adapt an existing model to different conditions such as
styles, sentiments or narratives, when facing different audiences or scenarios.
However, efficient adaptation of a language model to diverse conditions remains
an open challenge. This work is inspired by the observation that text
conditions are often associated with selection of certain words in a context.
Therefore we introduce LM-Switch, a theoretically grounded, lightweight and
simple method for generative language model conditioning. We begin by
investigating the effect of conditions in Hidden Markov Models (HMMs), and
establish a theoretical connection with language model. Our finding suggests
that condition shifts in HMMs are associated with linear transformations in
word embeddings. LM-Switch is then designed to deploy a learnable linear factor
in the word embedding space for language model conditioning. We show that
LM-Switch can model diverse tasks, and achieves comparable or better
performance compared with state-of-the-art baselines in LM detoxification and
generation control, despite requiring no more than 1% of parameters compared
with baselines and little extra time overhead compared with base LMs. It is
also able to learn from as few as a few sentences or one document. Moreover, a
learned LM-Switch can be transferred to other LMs of different sizes, achieving
a detoxification performance similar to the best baseline. We will make our
code available to the research community following publication.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.12907
|
2023-05-22T10:40:36Z
|
Meta-in-context learning in large language models
|
[
"Julian Coda-Forno",
"Marcel Binz",
"Zeynep Akata",
"Matthew Botvinick",
"Jane X. Wang",
"Eric Schulz"
] |
Large language models have shown tremendous performance in a variety of
tasks. In-context learning -- the ability to improve at a task after being
provided with a number of demonstrations -- is seen as one of the main
contributors to their success. In the present paper, we demonstrate that the
in-context learning abilities of large language models can be recursively
improved via in-context learning itself. We coin this phenomenon
meta-in-context learning. Looking at two idealized domains, a one-dimensional
regression task and a two-armed bandit task, we show that meta-in-context
learning adaptively reshapes a large language model's priors over expected
tasks. Furthermore, we find that meta-in-context learning modifies the
in-context learning strategies of such models. Finally, we extend our approach
to a benchmark of real-world regression problems where we observe competitive
performance to traditional learning algorithms. Taken together, our work
improves our understanding of in-context learning and paves the way toward
adapting large language models to the environment they are applied purely
through meta-in-context learning rather than traditional finetuning.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.12927
|
2023-05-22T11:14:19Z
|
Exploring Speaker-Related Information in Spoken Language Understanding
for Better Speaker Diarization
|
[
"Luyao Cheng",
"Siqi Zheng",
"Zhang Qinglin",
"Hui Wang",
"Yafeng Chen",
"Qian Chen"
] |
Speaker diarization(SD) is a classic task in speech processing and is crucial
in multi-party scenarios such as meetings and conversations. Current mainstream
speaker diarization approaches consider acoustic information only, which result
in performance degradation when encountering adverse acoustic conditions. In
this paper, we propose methods to extract speaker-related information from
semantic content in multi-party meetings, which, as we will show, can further
benefit speaker diarization. We introduce two sub-tasks, Dialogue Detection and
Speaker-Turn Detection, in which we effectively extract speaker information
from conversational semantics. We also propose a simple yet effective algorithm
to jointly model acoustic and semantic information and obtain
speaker-identified texts. Experiments on both AISHELL-4 and AliMeeting datasets
show that our method achieves consistent improvements over acoustic-only
speaker diarization systems.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.12995
|
2023-05-22T12:58:06Z
|
MaNtLE: Model-agnostic Natural Language Explainer
|
[
"Rakesh R. Menon",
"Kerem Zaman",
"Shashank Srivastava"
] |
Understanding the internal reasoning behind the predictions of machine
learning systems is increasingly vital, given their rising adoption and
acceptance. While previous approaches, such as LIME, generate algorithmic
explanations by attributing importance to input features for individual
examples, recent research indicates that practitioners prefer examining
language explanations that explain sub-groups of examples. In this paper, we
introduce MaNtLE, a model-agnostic natural language explainer that analyzes
multiple classifier predictions and generates faithful natural language
explanations of classifier rationale for structured classification tasks.
MaNtLE uses multi-task training on thousands of synthetic classification tasks
to generate faithful explanations. Simulated user studies indicate that, on
average, MaNtLE-generated explanations are at least 11% more faithful compared
to LIME and Anchors explanations across three tasks. Human evaluations
demonstrate that users can better predict model behavior using explanations
from MaNtLE compared to other techniques
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.13002
|
2023-05-22T13:07:35Z
|
Rethinking Semi-supervised Learning with Language Models
|
[
"Zhengxiang Shi",
"Francesco Tonolini",
"Nikolaos Aletras",
"Emine Yilmaz",
"Gabriella Kazai",
"Yunlong Jiao"
] |
Semi-supervised learning (SSL) is a popular setting aiming to effectively
utilize unlabelled data to improve model performance in downstream natural
language processing (NLP) tasks. Currently, there are two popular approaches to
make use of unlabelled data: Self-training (ST) and Task-adaptive pre-training
(TAPT). ST uses a teacher model to assign pseudo-labels to the unlabelled data,
while TAPT continues pre-training on the unlabelled data before fine-tuning. To
the best of our knowledge, the effectiveness of TAPT in SSL tasks has not been
systematically studied, and no previous work has directly compared TAPT and ST
in terms of their ability to utilize the pool of unlabelled data. In this
paper, we provide an extensive empirical study comparing five state-of-the-art
ST approaches and TAPT across various NLP tasks and data sizes, including in-
and out-of-domain settings. Surprisingly, we find that TAPT is a strong and
more robust SSL learner, even when using just a few hundred unlabelled samples
or in the presence of domain shifts, compared to more sophisticated ST
approaches, and tends to bring greater improvements in SSL than in
fully-supervised settings. Our further analysis demonstrates the risks of using
ST approaches when the size of labelled or unlabelled data is small or when
domain shifts exist. We offer a fresh perspective for future SSL research,
suggesting the use of unsupervised pre-training objectives over dependency on
pseudo labels.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.13052
|
2023-05-22T14:05:39Z
|
Federated Learning of Medical Concepts Embedding using BEHRT
|
[
"Ofir Ben Shoham",
"Nadav Rappoport"
] |
Electronic Health Records (EHR) data contains medical records such as
diagnoses, medications, procedures, and treatments of patients. This data is
often considered sensitive medical information. Therefore, the EHR data from
the medical centers often cannot be shared, making it difficult to create
prediction models using multi-center EHR data, which is essential for such
models' robustness and generalizability. Federated Learning (FL) is an
algorithmic approach that allows learning a shared model using data in multiple
locations without the need to store all data in a central place. An example of
a prediction model's task is to predict future diseases. More specifically, the
model needs to predict patient's next visit diagnoses, based on current and
previous clinical data. Such a prediction model can support care providers in
making clinical decisions and even provide preventive treatment. We propose a
federated learning approach for learning medical concepts embedding. This
pre-trained model can be used for fine-tuning for specific downstream tasks.
Our approach is based on an embedding model like BEHRT, a deep neural sequence
transduction model for EHR. We train using federated learning, both the Masked
Language Modeling (MLM) and the next visit downstream model. We demonstrate our
approach on the MIMIC-IV dataset. We compare the performance of a model trained
with FL against a model trained on centralized data. We find that our federated
learning approach reaches very close to the performance of a centralized model,
and it outperforms local models in terms of average precision. We also show
that pre-trained MLM improves the model's average precision performance in the
next visit prediction task, compared to an MLM model without pre-training. Our
code is available at https://github.com/nadavlab/FederatedBEHRT.
|
[
"cs.LG",
"cs.AI",
"cs.CL",
"cs.DC"
] | false |
2305.13080
|
2023-05-22T14:51:15Z
|
Mitigating Catastrophic Forgetting for Few-Shot Spoken Word
Classification Through Meta-Learning
|
[
"Ruan van der Merwe",
"Herman Kamper"
] |
We consider the problem of few-shot spoken word classification in a setting
where a model is incrementally introduced to new word classes. This would occur
in a user-defined keyword system where new words can be added as the system is
used. In such a continual learning scenario, a model might start to misclassify
earlier words as newer classes are added, i.e. catastrophic forgetting. To
address this, we propose an extension to model-agnostic meta-learning (MAML):
each inner learning loop, where a model "learns how to learn'' new classes,
ends with a single gradient update using stored templates from all the classes
that the model has already seen (one template per class). We compare this
method to OML (another extension of MAML) in few-shot isolated-word
classification experiments on Google Commands and FACC. Our method consistently
outperforms OML in experiments where the number of shots and the final number
of classes are varied.
|
[
"cs.CL",
"cs.AI",
"eess.AS",
"I.2.7; I.2.6"
] | false |
2305.13088
|
2023-05-22T14:54:21Z
|
Should We Attend More or Less? Modulating Attention for Fairness
|
[
"Abdelrahman Zayed",
"Goncalo Mordido",
"Samira Shabanian",
"Sarath Chandar"
] |
The abundance of annotated data in natural language processing (NLP) poses
both opportunities and challenges. While it enables the development of
high-performing models for a variety of tasks, it also poses the risk of models
learning harmful biases from the data, such as gender stereotypes. In this
work, we investigate the role of attention, a widely-used technique in current
state-of-the-art NLP models, in the propagation of social biases. Specifically,
we study the relationship between the entropy of the attention distribution and
the model's performance and fairness. We then propose a novel method for
modulating attention weights to improve model fairness after training. Since
our method is only applied post-training and pre-inference, it is an
intra-processing method and is, therefore, less computationally expensive than
existing in-processing and pre-processing approaches. Our results show an
increase in fairness and minimal performance loss on different text
classification and generation tasks using language models of varying sizes.
WARNING: This work uses language that is offensive.
|
[
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | false |
2305.13102
|
2023-05-22T15:04:16Z
|
Observations on LLMs for Telecom Domain: Capabilities and Limitations
|
[
"Sumit Soman",
"Ranjani H G"
] |
The landscape for building conversational interfaces (chatbots) has witnessed
a paradigm shift with recent developments in generative Artificial Intelligence
(AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and
GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In
this paper, we analyze capabilities and limitations of incorporating such
models in conversational interfaces for the telecommunication domain,
specifically for enterprise wireless products and services. Using Cradlepoint's
publicly available data for our experiments, we present a comparative analysis
of the responses from such models for multiple use-cases including domain
adaptation for terminology and product taxonomy, context continuity, robustness
to input perturbations and errors. We believe this evaluation would provide
useful insights to data scientists engaged in building customized
conversational interfaces for domain-specific requirements.
|
[
"cs.HC",
"cs.AI",
"cs.CL",
"cs.LG",
"68T50"
] | false |
2305.13191
|
2023-05-22T16:23:46Z
|
Taxonomy Expansion for Named Entity Recognition
|
[
"Karthikeyan K",
"Yogarshi Vyas",
"Jie Ma",
"Giovanni Paolini",
"Neha Anna John",
"Shuai Wang",
"Yassine Benajiba",
"Vittorio Castelli",
"Dan Roth",
"Miguel Ballesteros"
] |
Training a Named Entity Recognition (NER) model often involves fixing a
taxonomy of entity types. However, requirements evolve and we might need the
NER model to recognize additional entity types. A simple approach is to
re-annotate entire dataset with both existing and additional entity types and
then train the model on the re-annotated dataset. However, this is an extremely
laborious task. To remedy this, we propose a novel approach called Partial
Label Model (PLM) that uses only partially annotated datasets. We experiment
with 6 diverse datasets and show that PLM consistently performs better than
most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy
expansion not considered in prior work. The gap between PLM and all other
approaches is especially large in settings where there is limited data
available for the additional entity types (as much as 11 F1), thus suggesting a
more cost effective approaches to taxonomy expansion.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.13204
|
2023-05-22T16:36:04Z
|
Improving Isochronous Machine Translation with Target Factors and
Auxiliary Counters
|
[
"Proyag Pal",
"Brian Thompson",
"Yogesh Virkar",
"Prashant Mathur",
"Alexandra Chronopoulou",
"Marcello Federico"
] |
To translate speech for automatic dubbing, machine translation needs to be
isochronous, i.e. translated speech needs to be aligned with the source in
terms of speech durations. We introduce target factors in a transformer model
to predict durations jointly with target language phoneme sequences. We also
introduce auxiliary counters to help the decoder to keep track of the timing
information while generating target phonemes. We show that our model improves
translation quality and isochrony compared to previous work where the
translation model is instead trained to predict interleaved sequences of
phonemes and durations.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.13299
|
2023-05-22T17:56:31Z
|
Measuring Inductive Biases of In-Context Learning with Underspecified
Demonstrations
|
[
"Chenglei Si",
"Dan Friedman",
"Nitish Joshi",
"Shi Feng",
"Danqi Chen",
"He He"
] |
In-context learning (ICL) is an important paradigm for adapting large
language models (LLMs) to new tasks, but the generalization behavior of ICL
remains poorly understood. We investigate the inductive biases of ICL from the
perspective of feature bias: which feature ICL is more likely to use given a
set of underspecified demonstrations in which two features are equally
predictive of the labels. First, we characterize the feature biases of GPT-3
models by constructing underspecified demonstrations from a range of NLP
datasets and feature combinations. We find that LLMs exhibit clear feature
biases - for example, demonstrating a strong bias to predict labels according
to sentiment rather than shallow lexical features, like punctuation. Second, we
evaluate the effect of different interventions that are designed to impose an
inductive bias in favor of a particular feature, such as adding a natural
language instruction or using semantically relevant label words. We find that,
while many interventions can influence the learner to prefer a particular
feature, it can be difficult to overcome strong prior biases. Overall, our
results provide a broader picture of the types of features that ICL may be more
likely to exploit and how to impose inductive biases that are better aligned
with the intended task.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.13408
|
2023-05-22T18:49:35Z
|
Modular Domain Adaptation for Conformer-Based Streaming ASR
|
[
"Qiujia Li",
"Bo Li",
"Dongseong Hwang",
"Tara N. Sainath",
"Pedro M. Mengibar"
] |
Speech data from different domains has distinct acoustic and linguistic
characteristics. It is common to train a single multidomain model such as a
Conformer transducer for speech recognition on a mixture of data from all
domains. However, changing data in one domain or adding a new domain would
require the multidomain model to be retrained. To this end, we propose a
framework called modular domain adaptation (MDA) that enables a single model to
process multidomain data while keeping all parameters domain-specific, i.e.,
each parameter is only trained by data from one domain. On a streaming
Conformer transducer trained only on video caption data, experimental results
show that an MDA-based model can reach similar performance as the multidomain
model on other domains such as voice search and dictation by adding per-domain
adapters and per-domain feed-forward networks in the Conformer encoder.
|
[
"eess.AS",
"cs.CL",
"cs.LG",
"cs.SD"
] | false |
2305.13516
|
2023-05-22T22:09:41Z
|
Scaling Speech Technology to 1,000+ Languages
|
[
"Vineel Pratap",
"Andros Tjandra",
"Bowen Shi",
"Paden Tomasello",
"Arun Babu",
"Sayani Kundu",
"Ali Elkahky",
"Zhaoheng Ni",
"Apoorv Vyas",
"Maryam Fazel-Zarandi",
"Alexei Baevski",
"Yossi Adi",
"Xiaohui Zhang",
"Wei-Ning Hsu",
"Alexis Conneau",
"Michael Auli"
] |
Expanding the language coverage of speech technology has the potential to
improve access to information for many more people. However, current speech
technology is restricted to about one hundred languages which is a small
fraction of the over 7,000 languages spoken around the world. The Massively
Multilingual Speech (MMS) project increases the number of supported languages
by 10-40x, depending on the task. The main ingredients are a new dataset based
on readings of publicly available religious texts and effectively leveraging
self-supervised learning. We built pre-trained wav2vec 2.0 models covering
1,406 languages, a single multilingual automatic speech recognition model for
1,107 languages, speech synthesis models for the same number of languages, as
well as a language identification model for 4,017 languages. Experiments show
that our multilingual speech recognition model more than halves the word error
rate of Whisper on 54 languages of the FLEURS benchmark while being trained on
a small fraction of the labeled data.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.14386
|
2023-05-22T17:36:14Z
|
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with
Customized Exercise Generation
|
[
"Zhenwen Liang",
"Wenhao Yu",
"Tanmay Rajpurohit",
"Peter Clark",
"Xiangliang Zhang",
"Ashwin Kaylan"
] |
In this paper, we present a novel approach for distilling math word problem
solving capabilities from large language models (LLMs) into smaller, more
efficient student models. Our approach is designed to consider the student
model's weaknesses and foster a tailored learning experience by generating
targeted exercises aligned with educational science principles, such as
knowledge tracing and personalized learning. Concretely, we let GPT-3 be a math
tutor and run two steps iteratively: 1) assessing the student model's current
learning status on a GPT-generated exercise book, and 2) improving the student
model by training it with tailored exercise samples generated by GPT-3.
Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 and
PaLM) in accuracy across three distinct benchmarks while employing
significantly fewer parameters. Furthermore, we provide a comprehensive
analysis of the various components within our methodology to substantiate their
efficacy.
|
[
"cs.LG",
"cs.AI",
"cs.CL"
] | false |
2305.16333
|
2023-05-22T18:45:20Z
|
Text Generation with Speech Synthesis for ASR Data Augmentation
|
[
"Zhuangqun Huang",
"Gil Keren",
"Ziran Jiang",
"Shashank Jain",
"David Goss-Grubbs",
"Nelson Cheng",
"Farnaz Abtahi",
"Duc Le",
"David Zhang",
"Antony D'Avirro",
"Ethan Campbell-Taylor",
"Jessie Salas",
"Irina-Elena Veliche",
"Xi Chen"
] |
Aiming at reducing the reliance on expensive human annotations, data
synthesis for Automatic Speech Recognition (ASR) has remained an active area of
research. While prior work mainly focuses on synthetic speech generation for
ASR data augmentation, its combination with text generation methods is
considerably less explored. In this work, we explore text augmentation for ASR
using large-scale pre-trained neural networks, and systematically compare those
to traditional text augmentation methods. The generated synthetic texts are
then converted to synthetic speech using a text-to-speech (TTS) system and
added to the ASR training data. In experiments conducted on three datasets, we
find that neural models achieve 9%-15% relative WER improvement and outperform
traditional methods. We conclude that text augmentation, particularly through
modern neural approaches, is a viable tool for improving the accuracy of ASR
systems.
|
[
"cs.CL",
"cs.AI",
"cs.LG",
"eess.AS"
] | false |
2305.18319
|
2023-05-22T15:04:26Z
|
Automated Feedback Generation for a Chemistry Database and Abstracting
Exercise
|
[
"Oscar Morris",
"Russell Morris"
] |
Timely feedback is an important part of teaching and learning. Here we
describe how a readily available neural network transformer (machine-learning)
model (BERT) can be used to give feedback on the structure of the response to
an abstracting exercise where students are asked to summarise the contents of a
published article after finding it from a publication database. The dataset
contained 207 submissions from two consecutive years of the course, summarising
a total of 21 different papers from the primary literature. The model was
pre-trained using an available dataset (approx. 15,000 samples) and then
fine-tuned on 80% of the submitted dataset. This fine tuning was seen to be
important. The sentences in the student submissions are characterised into
three classes - background, technique and observation - which allows a
comparison of how each submission is structured. Comparing the structure of the
students' abstract a large collection of those from the PubMed database shows
that students in this exercise concentrate more on the background to the paper
and less on the techniques and results than the abstracts to papers themselves.
The results allowed feedback for each submitted assignment to be automatically
generated.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.18320
|
2023-05-22T15:06:51Z
|
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4
mirroring math anxiety in high-school students
|
[
"Katherine Abramski",
"Salvatore Citraro",
"Luigi Lombardi",
"Giulio Rossetti",
"Massimo Stella"
] |
Large language models are becoming increasingly integrated into our lives.
Hence, it is important to understand the biases present in their outputs in
order to avoid perpetuating harmful stereotypes, which originate in our own
flawed ways of thinking. This challenge requires developing new benchmarks and
methods for quantifying affective and semantic bias, keeping in mind that LLMs
act as psycho-social mirrors that reflect the views and tendencies that are
prevalent in society. One such tendency that has harmful negative effects is
the global phenomenon of anxiety toward math and STEM subjects. Here, we
investigate perceptions of math and STEM fields provided by cutting-edge
language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach
from network science and cognitive psychology. Specifically, we use behavioral
forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM
disciplines in relation to other concepts. We use data obtained by probing the
three LLMs in a language generation task that has previously been applied to
humans. Our findings indicate that LLMs have an overall negative perception of
math and STEM fields, with math being perceived most negatively. We observe
significant differences across the three LLMs. We observe that newer versions
(i.e. GPT-4) produce richer, more complex perceptions as well as less negative
perceptions compared to older versions and N=159 high-school students. These
findings suggest that advances in the architecture of LLMs may lead to
increasingly less biased models that could even perhaps someday aid in reducing
harmful stereotypes in society rather than perpetuating them.
|
[
"cs.CY",
"cs.AI",
"cs.CL"
] | false |
2305.18569
|
2023-05-22T17:51:56Z
|
Fairness of ChatGPT
|
[
"Yunqi Li",
"Yongfeng Zhang"
] |
Understanding and addressing unfairness in LLMs are crucial for responsible
AI deployment. However, there is a limited availability of quantitative
analyses and in-depth studies regarding fairness evaluations in LLMs,
especially when applying LLMs to high-stakes fields. This work aims to fill
this gap by providing a systematic evaluation of the effectiveness and fairness
of LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's
performance in high-takes fields including education, criminology, finance and
healthcare. To make thorough evaluation, we consider both group fairness and
individual fairness and we also observe the disparities in ChatGPT's outputs
under a set of biased or unbiased prompts. This work contributes to a deeper
understanding of LLMs' fairness performance, facilitates bias mitigation and
fosters the development of responsible artificial intelligence systems.
|
[
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CY"
] | false |
2305.12868
|
2023-05-22T09:46:10Z
|
NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound
Synthesis based on Frequency Modulation
|
[
"Zhen Ye",
"Wei Xue",
"Xu Tan",
"Qifeng Liu",
"Yike Guo"
] |
Developing digital sound synthesizers is crucial to the music industry as it
provides a low-cost way to produce high-quality sounds with rich timbres.
Existing traditional synthesizers often require substantial expertise to
determine the overall framework of a synthesizer and the parameters of
submodules. Since expert knowledge is hard to acquire, it hinders the
flexibility to quickly design and tune digital synthesizers for diverse sounds.
In this paper, we propose ``NAS-FM'', which adopts neural architecture search
(NAS) to build a differentiable frequency modulation (FM) synthesizer. Tunable
synthesizers with interpretable controls can be developed automatically from
sounds without any prior expert knowledge and manual operating costs. In
detail, we train a supernet with a specifically designed search space,
including predicting the envelopes of carriers and modulators with different
frequency ratios. An evolutionary search algorithm with adaptive oscillator
size is then developed to find the optimal relationship between oscillators and
the frequency ratio of FM. Extensive experiments on recordings of different
instrument sounds show that our algorithm can build a synthesizer fully
automatically, achieving better results than handcrafted synthesizers. Audio
samples are available at https://nas-fm.github.io/.
|
[
"cs.SD",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM",
"eess.AS"
] | false |
2305.12677
|
2023-05-22T03:29:42Z
|
Tokenized Graph Transformer with Neighborhood Augmentation for Node
Classification in Large Graphs
|
[
"Jinsong Chen",
"Chang Liu",
"Kaiyuan Gao",
"Gaichao Li",
"Kun He"
] |
Graph Transformers, emerging as a new architecture for graph representation
learning, suffer from the quadratic complexity on the number of nodes when
handling large graphs. To this end, we propose a Neighborhood Aggregation Graph
Transformer (NAGphormer) that treats each node as a sequence containing a
series of tokens constructed by our proposed Hop2Token module. For each node,
Hop2Token aggregates the neighborhood features from different hops into
different representations, producing a sequence of token vectors as one input.
In this way, NAGphormer could be trained in a mini-batch manner and thus could
scale to large graphs. Moreover, we mathematically show that compared to a
category of advanced Graph Neural Networks (GNNs), called decoupled Graph
Convolutional Networks, NAGphormer could learn more informative node
representations from multi-hop neighborhoods. In addition, we propose a new
data augmentation method called Neighborhood Augmentation (NrAug) based on the
output of Hop2Token that augments simultaneously the features of neighborhoods
from global as well as local views to strengthen the training effect of
NAGphormer. Extensive experiments on benchmark datasets from small to large
demonstrate the superiority of NAGphormer against existing graph Transformers
and mainstream GNNs, and the effectiveness of NrAug for further boosting
NAGphormer.
|
[
"cs.LG"
] | false |
2305.12895
|
2023-05-22T10:29:52Z
|
DEGREE: Decomposition Based Explanation For Graph Neural Networks
|
[
"Qizhang Feng",
"Ninghao Liu",
"Fan Yang",
"Ruixiang Tang",
"Mengnan Du",
"Xia Hu"
] |
Graph Neural Networks (GNNs) are gaining extensive attention for their
application in graph data. However, the black-box nature of GNNs prevents users
from understanding and trusting the models, thus hampering their applicability.
Whereas explaining GNNs remains a challenge, most existing methods fall into
approximation based and perturbation based approaches with suffer from
faithfulness problems and unnatural artifacts, respectively. To tackle these
problems, we propose DEGREE \degree to provide a faithful explanation for GNN
predictions. By decomposing the information generation and aggregation
mechanism of GNNs, DEGREE allows tracking the contributions of specific
components of the input graph to the final prediction. Based on this, we
further design a subgraph level interpretation algorithm to reveal complex
interactions between graph nodes that are overlooked by previous methods. The
efficiency of our algorithm can be further improved by utilizing GNN
characteristics. Finally, we conduct quantitative and qualitative experiments
on synthetic and real-world datasets to demonstrate the effectiveness of DEGREE
on node classification and graph classification tasks.
|
[
"cs.LG"
] | false |
2305.12906
|
2023-05-22T10:39:54Z
|
Latent Magic: An Investigation into Adversarial Examples Crafted in the
Semantic Latent Space
|
[
"BoYang Zheng"
] |
Adversarial attacks against Deep Neural Networks(DNN) have been a crutial
topic ever since \cite{goodfellow} purposed the vulnerability of DNNs. However,
most prior works craft adversarial examples in the pixel space, following the
$l_p$ norm constraint. In this paper, we give intuitional explain about why
crafting adversarial examples in the latent space is equally efficient and
important. We purpose a framework for crafting adversarial examples in semantic
latent space based on an pre-trained Variational Auto Encoder from state-of-art
Stable Diffusion Model\cite{SDM}. We also show that adversarial examples
crafted in the latent space can also achieve a high level of fool rate.
However, examples crafted from latent space are often hard to evaluated, as
they doesn't follow a certain $l_p$ norm constraint, which is a big challenge
for existing researches. To efficiently and accurately evaluate the adversarial
examples crafted in the latent space, we purpose \textbf{a novel evaluation
matric} based on SSIM\cite{SSIM} loss and fool rate.Additionally, we explain
why FID\cite{FID} is not suitable for measuring such adversarial examples. To
the best of our knowledge, it's the first evaluation metrics that is
specifically designed to evaluate the quality of a adversarial attack. We also
investigate the transferability of adversarial examples crafted in the latent
space and show that they have superiority over adversarial examples crafted in
the pixel space.
|
[
"cs.LG"
] | false |
2305.12944
|
2023-05-22T11:45:23Z
|
Offline Primal-Dual Reinforcement Learning for Linear MDPs
|
[
"Germano Gabbianelli",
"Gergely Neu",
"Nneka Okolo",
"Matteo Papini"
] |
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from
a fixed dataset of transitions collected by another policy. This problem has
attracted a lot of attention recently, but most existing methods with strong
theoretical guarantees are restricted to finite-horizon or tabular settings. In
constrast, few algorithms for infinite-horizon settings with function
approximation and minimal assumptions on the dataset are both sample and
computationally efficient. Another gap in the current literature is the lack of
theoretical analysis for the average-reward setting, which is more challenging
than the discounted setting. In this paper, we address both of these issues by
proposing a primal-dual optimization method based on the linear programming
formulation of RL. Our key contribution is a new reparametrization that allows
us to derive low-variance gradient estimators that can be used in a stochastic
optimization scheme using only samples from the behavior policy. Our method
finds an $\varepsilon$-optimal policy with $O(\varepsilon^{-4})$ samples,
improving on the previous $O(\varepsilon^{-5})$, while being computationally
efficient for infinite-horizon discounted and average-reward MDPs with
realizable linear function approximation and partial coverage. Moreover, to the
best of our knowledge, this is the first theoretical result for average-reward
offline RL.
|
[
"cs.LG"
] | false |
2305.12958
|
2023-05-22T12:09:14Z
|
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly
Detection
|
[
"Jonas Soenen",
"Elia Van Wolputte",
"Vincent Vercruyssen",
"Wannes Meert",
"Hendrik Blockeel"
] |
Most anomaly detection systems try to model normal behavior and assume
anomalies deviate from it in diverse manners. However, there may be patterns in
the anomalies as well. Ideally, an anomaly detection system can exploit
patterns in both normal and anomalous behavior. In this paper, we present
AD-MERCS, an unsupervised approach to anomaly detection that explicitly aims at
doing both. AD-MERCS identifies multiple subspaces of the instance space within
which patterns exist, and identifies conditions (possibly in other subspaces)
that characterize instances that deviate from these patterns. Experiments show
that this modeling of both normality and abnormality makes the anomaly detector
performant on a wide range of types of anomalies. Moreover, by identifying
patterns and conditions in (low-dimensional) subspaces, the anomaly detector
can provide simple explanations of why something is considered an anomaly.
These explanations can be both negative (deviation from some pattern) as
positive (meeting some condition that is typical for anomalies).
|
[
"cs.LG"
] | false |
2305.12985
|
2023-05-22T12:44:38Z
|
Feasibility of Transfer Learning: A Mathematical Framework
|
[
"Haoyang Cao",
"Haotian Gu",
"Xin Guo"
] |
Transfer learning is a popular paradigm for utilizing existing knowledge from
previous learning tasks to improve the performance of new ones. It has enjoyed
numerous empirical successes and inspired a growing number of theoretical
studies. This paper addresses the feasibility issue of transfer learning. It
begins by establishing the necessary mathematical concepts and constructing a
mathematical framework for transfer learning. It then identifies and formulates
the three-step transfer learning procedure as an optimization problem, allowing
for the resolution of the feasibility issue. Importantly, it demonstrates that
under certain technical conditions, such as appropriate choice of loss
functions and data sets, an optimal procedure for transfer learning exists.
This study of the feasibility issue brings additional insights into various
transfer learning problems. It sheds light on the impact of feature
augmentation on model performance, explores potential extensions of domain
adaptation, and examines the feasibility of efficient feature extractor
transfer in image classification.
|
[
"cs.LG"
] | false |
2305.13063
|
2023-05-22T14:25:46Z
|
Hierarchical Partitioning Forecaster
|
[
"Christopher Mattern"
] |
In this work we consider a new family of algorithms for sequential
prediction, Hierarchical Partitioning Forecasters (HPFs). Our goal is to
provide appealing theoretical - regret guarantees on a powerful model class -
and practical - empirical performance comparable to deep networks - properties
at the same time. We built upon three principles: hierarchically partitioning
the feature space into sub-spaces, blending forecasters specialized to each
sub-space and learning HPFs via local online learning applied to these
individual forecasters. Following these principles allows us to obtain regret
guarantees, where Constant Partitioning Forecasters (CPFs) serve as competitor.
A CPF partitions the feature space into sub-spaces and predicts with a fixed
forecaster per sub-space. Fixing a hierarchical partition $\mathcal H$ and
considering any CPF with a partition that can be constructed using elements of
$\mathcal H$ we provide two guarantees: first, a generic one that unveils how
local online learning determines regret of learning the entire HPF online;
second, a concrete instance that considers HPF with linear forecasters (LHPF)
and exp-concave losses where we obtain $O(k \log T)$ regret for sequences of
length $T$ where $k$ is a measure of complexity for the competing CPF. Finally,
we provide experiments that compare LHPF to various baselines, including state
of the art deep learning models, in precipitation nowcasting. Our results
indicate that LHPF is competitive in various settings.
|
[
"cs.LG"
] | false |
2305.13072
|
2023-05-22T14:41:17Z
|
Breaking the Paradox of Explainable Deep Learning
|
[
"Arlind Kadra",
"Sebastian Pineda Arango",
"Josif Grabocka"
] |
Deep Learning has achieved tremendous results by pushing the frontier of
automation in diverse domains. Unfortunately, current neural network
architectures are not explainable by design. In this paper, we propose a novel
method that trains deep hypernetworks to generate explainable linear models.
Our models retain the accuracy of black-box deep networks while offering free
lunch explainability by design. Specifically, our explainable approach requires
the same runtime and memory resources as black-box deep models, ensuring
practical feasibility. Through extensive experiments, we demonstrate that our
explainable deep networks are as accurate as state-of-the-art classifiers on
tabular data. On the other hand, we showcase the interpretability of our method
on a recent benchmark by empirically comparing prediction explainers. The
experimental results reveal that our models are not only as accurate as their
black-box deep-learning counterparts but also as interpretable as
state-of-the-art explanation techniques.
|
[
"cs.LG"
] | false |
2305.13122
|
2023-05-22T15:23:41Z
|
Policy Representation via Diffusion Probability Model for Reinforcement
Learning
|
[
"Long Yang",
"Zhixiong Huang",
"Fenghao Lei",
"Yucun Zhong",
"Yiming Yang",
"Cong Fang",
"Shiting Wen",
"Binbin Zhou",
"Zhouchen Lin"
] |
Popular reinforcement learning (RL) algorithms tend to produce a unimodal
policy distribution, which weakens the expressiveness of complicated policy and
decays the ability of exploration. The diffusion probability model is powerful
to learn complicated multimodal distributions, which has shown promising and
potential applications to RL. In this paper, we formally build a theoretical
foundation of policy representation via the diffusion probability model and
provide practical implementations of diffusion policy for online model-free RL.
Concretely, we character diffusion policy as a stochastic process, which is a
new approach to representing a policy. Then we present a convergence guarantee
for diffusion policy, which provides a theory to understand the multimodality
of diffusion policy. Furthermore, we propose the DIPO which is an
implementation for model-free online RL with DIffusion POlicy. To the best of
our knowledge, DIPO is the first algorithm to solve model-free online RL
problems with the diffusion model. Finally, extensive empirical results show
the effectiveness and superiority of DIPO on the standard continuous control
Mujoco benchmark.
|
[
"cs.LG"
] | false |
2305.13170
|
2023-05-22T15:58:01Z
|
Explicit Personalization and Local Training: Double Communication
Acceleration in Federated Learning
|
[
"Kai Yi",
"Laurent Condat",
"Peter Richtárik"
] |
Federated Learning is an evolving machine learning paradigm, in which
multiple clients perform computations based on their individual private data,
interspersed by communication with a remote server. A common strategy to
curtail communication costs is Local Training, which consists in performing
multiple local stochastic gradient descent steps between successive
communication rounds. However, the conventional approach to local training
overlooks the practical necessity for client-specific personalization, a
technique to tailor local models to individual needs. We introduce Scafflix, a
novel algorithm that efficiently integrates explicit personalization with local
training. This innovative approach benefits from these two techniques, thereby
achieving doubly accelerated communication, as we demonstrate both in theory
and practice.
|
[
"cs.LG"
] | false |
2305.13185
|
2023-05-22T16:13:05Z
|
Regularization and Variance-Weighted Regression Achieves Minimax
Optimality in Linear MDPs: Theory and Practice
|
[
"Toshinori Kitamura",
"Tadashi Kozuno",
"Yunhao Tang",
"Nino Vieillard",
"Michal Valko",
"Wenhao Yang",
"Jincheng Mei",
"Pierre Ménard",
"Mohammad Gheshlaghi Azar",
"Rémi Munos",
"Olivier Pietquin",
"Matthieu Geist",
"Csaba Szepesvári",
"Wataru Kumagai",
"Yutaka Matsuo"
] |
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler
(KL) and entropy-regularized reinforcement learning (RL), has served as the
basis for recent high-performing practical RL algorithms. However, despite the
use of function approximation in practice, the theoretical understanding of
MDVI has been limited to tabular Markov decision processes (MDPs). We study
MDVI with linear function approximation through its sample complexity required
to identify an $\varepsilon$-optimal policy with probability $1-\delta$ under
the settings of an infinite-horizon linear MDP, generative model, and G-optimal
design. We demonstrate that least-squares regression weighted by the variance
of an estimated optimal value function of the next state is crucial to
achieving minimax optimality. Based on this observation, we present
Variance-Weighted Least-Squares MDVI (VWLS-MDVI), the first theoretical
algorithm that achieves nearly minimax optimal sample complexity for
infinite-horizon linear MDPs. Furthermore, we propose a practical VWLS
algorithm for value-based deep RL, Deep Variance Weighting (DVW). Our
experiments demonstrate that DVW improves the performance of popular
value-based deep RL algorithms on a set of MinAtar benchmarks.
|
[
"cs.LG"
] | false |
2305.13275
|
2023-05-22T17:36:21Z
|
A Machine Learning Approach to Detect Dehydration in Afghan Children
|
[
"Ziaullah Momand",
"Debajyoti Pal",
"Pornchai Mongkolnam",
"Jonathan H. Chan"
] |
Child dehydration is a significant health concern, especially among children
under 5 years of age who are more susceptible to diarrhea and vomiting. In
Afghanistan, severe diarrhea contributes to child mortality due to dehydration.
However, there is no evidence of research exploring the potential of machine
learning techniques in diagnosing dehydration in Afghan children under five. To
fill this gap, this study leveraged various classifiers such as Random Forest,
Multilayer Perceptron, Support Vector Machine, J48, and Logistic Regression to
develop a predictive model using a dataset of sick children retrieved from the
Afghanistan Demographic and Health Survey (ADHS). The primary objective was to
determine the dehydration status of children under 5 years. Among all the
classifiers, Random Forest proved to be the most effective, achieving an
accuracy of 91.46%, precision of 91%, and AUC of 94%. This model can
potentially assist healthcare professionals in promptly and accurately
identifying dehydration in under five children, leading to timely
interventions, and reducing the risk of severe health complications. Our study
demonstrates the potential of machine learning techniques in improving the
early diagnosis of dehydration in Afghan children.
|
[
"cs.LG"
] | false |
2305.13290
|
2023-05-22T17:50:42Z
|
Uncertainty and Structure in Neural Ordinary Differential Equations
|
[
"Katharina Ott",
"Michael Tiemann",
"Philipp Hennig"
] |
Neural ordinary differential equations (ODEs) are an emerging class of deep
learning models for dynamical systems. They are particularly useful for
learning an ODE vector field from observed trajectories (i.e., inverse
problems). We here consider aspects of these models relevant for their
application in science and engineering. Scientific predictions generally
require structured uncertainty estimates. As a first contribution, we show that
basic and lightweight Bayesian deep learning techniques like the Laplace
approximation can be applied to neural ODEs to yield structured and meaningful
uncertainty quantification. But, in the scientific domain, available
information often goes beyond raw trajectories, and also includes mechanistic
knowledge, e.g., in the form of conservation laws. We explore how mechanistic
knowledge and uncertainty quantification interact on two recently proposed
neural ODE frameworks - symplectic neural ODEs and physical models augmented
with neural ODEs. In particular, uncertainty reflects the effect of mechanistic
information more directly than the predictive power of the trained model could.
And vice versa, structure can improve the extrapolation abilities of neural
ODEs, a fact that can be best assessed in practice through uncertainty
estimates. Our experimental analysis demonstrates the effectiveness of the
Laplace approach on both low dimensional ODE problems and a high dimensional
partial differential equation.
|
[
"cs.LG"
] | false |
2305.13508
|
2023-05-22T21:52:57Z
|
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using
Bernstein Polynomial Activations and Precise Bound Propagation
|
[
"Haitham Khedr",
"Yasser Shoukry"
] |
Formal certification of Neural Networks (NNs) is crucial for ensuring their
safety, fairness, and robustness. Unfortunately, on the one hand, sound and
complete certification algorithms of ReLU-based NNs do not scale to large-scale
NNs. On the other hand, incomplete certification algorithms are easier to
compute, but they result in loose bounds that deteriorate with the depth of NN,
which diminishes their effectiveness. In this paper, we ask the following
question; can we replace the ReLU activation function with one that opens the
door to incomplete certification algorithms that are easy to compute but can
produce tight bounds on the NN's outputs? We introduce DeepBern-Nets, a class
of NNs with activation functions based on Bernstein polynomials instead of the
commonly used ReLU activation. Bernstein polynomials are smooth and
differentiable functions with desirable properties such as the so-called range
enclosure and subdivision properties. We design a novel algorithm, called
Bern-IBP, to efficiently compute tight bounds on DeepBern-Nets outputs. Our
approach leverages the properties of Bernstein polynomials to improve the
tractability of neural network certification tasks while maintaining the
accuracy of the trained networks. We conduct comprehensive experiments in
adversarial robustness and reachability analysis settings to assess the
effectiveness of the proposed Bernstein polynomial activation in enhancing the
certification process. Our proposed framework achieves high certified accuracy
for adversarially-trained NNs, which is often a challenging task for certifiers
of ReLU-based NNs. Moreover, using Bern-IBP bounds for certified training
results in NNs with state-of-the-art certified accuracy compared to ReLU
networks. This work establishes Bernstein polynomial activation as a promising
alternative for improving NN certification tasks across various applications.
|
[
"cs.LG"
] | false |
2305.12663
|
2023-05-22T03:06:09Z
|
TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning
via Transition Occupancy Matching
|
[
"Yecheng Jason Ma",
"Kausik Sivakumar",
"Jason Yan",
"Osbert Bastani",
"Dinesh Jayaraman"
] |
Standard model-based reinforcement learning (MBRL) approaches fit a
transition model of the environment to all past experience, but this wastes
model capacity on data that is irrelevant for policy improvement. We instead
propose a new "transition occupancy matching" (TOM) objective for MBRL model
learning: a model is good to the extent that the current policy experiences the
same distribution of transitions inside the model as in the real environment.
We derive TOM directly from a novel lower bound on the standard reinforcement
learning objective. To optimize TOM, we show how to reduce it to a form of
importance weighted maximum-likelihood estimation, where the automatically
computed importance weights identify policy-relevant past experiences from a
replay buffer, enabling stable optimization. TOM thus offers a plug-and-play
model learning sub-routine that is compatible with any backbone MBRL algorithm.
On various Mujoco continuous robotic control tasks, we show that TOM
successfully focuses model learning on policy-relevant experience and drives
policies faster to higher task rewards than alternative model learning
approaches.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.12686
|
2023-05-22T03:48:38Z
|
Conformal Inference for Invariant Risk Minimization
|
[
"Wenlu Tang",
"Zicheng Liu"
] |
The application of machine learning models can be significantly impeded by
the occurrence of distributional shifts, as the assumption of homogeneity
between the population of training and testing samples in machine learning and
statistics may not be feasible in practical situations. One way to tackle this
problem is to use invariant learning, such as invariant risk minimization
(IRM), to acquire an invariant representation that aids in generalization with
distributional shifts. This paper develops methods for obtaining
distribution-free prediction regions to describe uncertainty estimates for
invariant representations, accounting for the distribution shifts of data from
different environments. Our approach involves a weighted conformity score that
adapts to the specific environment in which the test sample is situated. We
construct an adaptive conformal interval using the weighted conformity score
and prove its conditional average under certain conditions. To demonstrate the
effectiveness of our approach, we conduct several numerical experiments,
including simulation studies and a practical example using real-world data.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.12783
|
2023-05-22T07:27:37Z
|
Quantum Text Classifier -- A Synchronistic Approach Towards Classical
and Quantum Machine Learning
|
[
"Dr. Prabhat Santi",
"Kamakhya Mishra",
"Sibabrata Mohanty"
] |
Although it will be a while before a practical quantum computer is available,
there is no need to hold off. Methods and algorithms are being developed to
demonstrate the feasibility of running machine learning (ML) pipelines in QC
(Quantum Computing). There is a lot of ongoing work on general QML (Quantum
Machine Learning) algorithms and applications. However, a working model or
pipeline for a text classifier using quantum algorithms isn't available. This
paper introduces quantum machine learning w.r.t text classification to readers
of classical machine learning. It begins with a brief description of quantum
computing and basic quantum algorithms, with an emphasis on building text
classification pipelines. A new approach is introduced to implement an
end-to-end text classification framework (Quantum Text Classifier - QTC), where
pre- and post-processing of data is performed on a classical computer, and text
classification is performed using the QML algorithm. This paper also presents
an implementation of the QTC framework and available quantum ML algorithms for
text classification using the IBM Qiskit library and IBM backends.
|
[
"quant-ph",
"cs.LG"
] | false |
2305.12922
|
2023-05-22T11:09:49Z
|
It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for
Recommendation
|
[
"Jaewan Moon",
"Hye-young Kim",
"Jongwuk Lee"
] |
Linear autoencoder models learn an item-to-item weight matrix via convex
optimization with L2 regularization and zero-diagonal constraints. Despite
their simplicity, they have shown remarkable performance compared to
sophisticated non-linear models. This paper aims to theoretically understand
the properties of two terms in linear autoencoders. Through the lens of
singular value decomposition (SVD) and principal component analysis (PCA), it
is revealed that L2 regularization enhances the impact of high-ranked PCs.
Meanwhile, zero-diagonal constraints reduce the impact of low-ranked PCs,
leading to performance degradation for unpopular items. Inspired by this
analysis, we propose simple-yet-effective linear autoencoder models using
diagonal inequality constraints, called Relaxed Linear AutoEncoder (RLAE) and
Relaxed Denoising Linear AutoEncoder (RDLAE). We prove that they generalize
linear autoencoders by adjusting the degree of diagonal constraints.
Experimental results demonstrate that our models are comparable or superior to
state-of-the-art linear and non-linear models on six benchmark datasets; they
significantly improve the accuracy of long-tail items. These results also
support our theoretical insights on regularization and diagonal constraints in
linear autoencoders.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.12976
|
2023-05-22T12:32:06Z
|
Attentive Graph-based Text-aware Preference Modeling for Top-N
Recommendation
|
[
"Ming-Hao Juan",
"Pu-Jen Cheng",
"Hui-Neng Hsu",
"Pin-Hsin Hsiao"
] |
Textual data are commonly used as auxiliary information for modeling user
preference nowadays. While many prior works utilize user reviews for rating
prediction, few focus on top-N recommendation, and even few try to incorporate
item textual contents such as title and description. Though delivering
promising performance for rating prediction, we empirically find that many
review-based models cannot perform comparably well on top-N recommendation.
Also, user reviews are not available in some recommendation scenarios, while
item textual contents are more prevalent. On the other hand, recent graph
convolutional network (GCN) based models demonstrate state-of-the-art
performance for top-N recommendation. Thus, in this work, we aim to further
improve top-N recommendation by effectively modeling both item textual content
and high-order connectivity in user-item graph. We propose a new model named
Attentive Graph-based Text-aware Recommendation Model (AGTM). Extensive
experiments are provided to justify the rationality and effectiveness of our
model design.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.12979
|
2023-05-22T12:36:52Z
|
When Computing Power Network Meets Distributed Machine Learning: An
Efficient Federated Split Learning Framework
|
[
"Xinjing Yuan",
"Lingjun Pu",
"Lei Jiao",
"Xiaofei Wang",
"Meijuan Yang",
"Jingdong Xu"
] |
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split
Learning (FedSL) framework over Computing Power Network (CPN). We build a
dedicated model to capture the basic settings and learning characteristics
(e.g., training flow, latency and convergence). Based on this model, we
introduce Resource Usage Effectiveness (RUE), a novel performance metric
integrating training utility with system cost, and formulate a multivariate
scheduling problem that maxi?mizes RUE by comprehensively taking client
admission, model partition, server selection, routing and bandwidth allocation
into account (i.e., mixed-integer fractional programming). We design Refinery,
an efficient approach that first linearizes the fractional objective and
non-convex constraints, and then solves the transformed problem via a greedy
based rounding algorithm in multiple iterations. Extensive evaluations
corroborate that CPN-FedSL is superior to the standard and state-of-the-art
learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is
lightweight and significantly outperforms its variants and de facto heuristic
methods under a variety of settings.
|
[
"cs.NI",
"cs.LG"
] | false |
2305.13165
|
2023-05-22T15:51:28Z
|
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained
Features Model
|
[
"Peter Súkeník",
"Marco Mondelli",
"Christoph Lampert"
] |
Neural collapse (NC) refers to the surprising structure of the last layer of
deep neural networks in the terminal phase of gradient descent training.
Recently, an increasing amount of experimental evidence has pointed to the
propagation of NC to earlier layers of neural networks. However, while the NC
in the last layer is well studied theoretically, much less is known about its
multi-layered counterpart - deep neural collapse (DNC). In particular, existing
work focuses either on linear layers or only on the last two layers at the
price of an extra assumption. Our paper fills this gap by generalizing the
established analytical framework for NC - the unconstrained features model - to
multiple non-linear layers. Our key technical contribution is to show that, in
a deep unconstrained features model, the unique global optimum for binary
classification exhibits all the properties typical of DNC. This explains the
existing experimental evidence of DNC. We also empirically show that (i) by
optimizing deep unconstrained features models via gradient descent, the
resulting solution agrees well with our theory, and (ii) trained networks
recover the unconstrained features suitable for the occurrence of DNC, thus
supporting the validity of this modeling principle.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.13226
|
2023-05-22T16:58:26Z
|
Sequential Transfer Learning to Decode Heard and Imagined Timbre from
fMRI Data
|
[
"Sean Paulsen",
"Michael Casey"
] |
We present a sequential transfer learning framework for transformers on
functional Magnetic Resonance Imaging (fMRI) data and demonstrate its
significant benefits for decoding musical timbre. In the first of two phases,
we pre-train our stacked-encoder transformer architecture on Next Thought
Prediction, a self-supervised task of predicting whether or not one sequence of
fMRI data follows another. This phase imparts a general understanding of the
temporal and spatial dynamics of neural activity, and can be applied to any
fMRI dataset. In the second phase, we fine-tune the pre-trained models and
train additional fresh models on the supervised task of predicting whether or
not two sequences of fMRI data were recorded while listening to the same
musical timbre. The fine-tuned models achieve significantly higher accuracy
with shorter training times than the fresh models, demonstrating the efficacy
of our framework for facilitating transfer learning on fMRI data. Additionally,
our fine-tuning task achieves a level of classification granularity beyond
standard methods. This work contributes to the growing literature on
transformer architectures for sequential transfer learning on fMRI data, and
provides evidence that our framework is an improvement over current methods for
decoding timbre.
|
[
"q-bio.QM",
"cs.LG"
] | false |
2305.13271
|
2023-05-22T17:34:47Z
|
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of
Deep Neural Networks
|
[
"Felix Hensel",
"Charles Arnal",
"Mathieu Carrière",
"Théo Lacombe",
"Hiroaki Kurihara",
"Yuichi Ike",
"Frédéric Chazal"
] |
Despite their successful application to a variety of tasks, neural networks
remain limited, like other machine learning methods, by their sensitivity to
shifts in the data: their performance can be severely impacted by differences
in distribution between the data on which they were trained and that on which
they are deployed. In this article, we propose a new family of representations,
called MAGDiff, that we extract from any given neural network classifier and
that allows for efficient covariate data shift detection without the need to
train a new model dedicated to this task. These representations are computed by
comparing the activation graphs of the neural network for samples belonging to
the training distribution and to the target distribution, and yield powerful
data- and task-adapted statistics for the two-sample tests commonly used for
data set shift detection. We demonstrate this empirically by measuring the
statistical powers of two-sample Kolmogorov-Smirnov (KS) tests on several
different data sets and shift types, and showing that our novel representations
induce significant improvements over a state-of-the-art baseline relying on the
network output.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.13283
|
2023-05-22T17:43:34Z
|
Approximating a RUM from Distributions on k-Slates
|
[
"Flavio Chierichetti",
"Mirko Giacchini",
"Ravi Kumar",
"Alessandro Panconesi",
"Andrew Tomkins"
] |
In this work we consider the problem of fitting Random Utility Models (RUMs)
to user choices. Given the winner distributions of the subsets of size $k$ of a
universe, we obtain a polynomial-time algorithm that finds the RUM that best
approximates the given distribution on average. Our algorithm is based on a
linear program that we solve using the ellipsoid method. Given that its
corresponding separation oracle problem is NP-hard, we devise an approximate
separation oracle that can be viewed as a generalization of the weighted
feedback arc set problem to hypergraphs. Our theoretical result can also be
made practical: we obtain a heuristic that is effective and scales to
real-world datasets.
|
[
"cs.LG",
"cs.DS"
] | false |
2305.13362
|
2023-05-22T18:00:02Z
|
On quantum backpropagation, information reuse, and cheating measurement
collapse
|
[
"Amira Abbas",
"Robbie King",
"Hsin-Yuan Huang",
"William J. Huggins",
"Ramis Movassagh",
"Dar Gilboa",
"Jarrod R. McClean"
] |
The success of modern deep learning hinges on the ability to train neural
networks at scale. Through clever reuse of intermediate information,
backpropagation facilitates training through gradient computation at a total
cost roughly proportional to running the function, rather than incurring an
additional factor proportional to the number of parameters - which can now be
in the trillions. Naively, one expects that quantum measurement collapse
entirely rules out the reuse of quantum information as in backpropagation. But
recent developments in shadow tomography, which assumes access to multiple
copies of a quantum state, have challenged that notion. Here, we investigate
whether parameterized quantum models can train as efficiently as classical
neural networks. We show that achieving backpropagation scaling is impossible
without access to multiple copies of a state. With this added ability, we
introduce an algorithm with foundations in shadow tomography that matches
backpropagation scaling in quantum resources while reducing classical auxiliary
computational costs to open problems in shadow tomography. These results
highlight the nuance of reusing quantum information for practical purposes and
clarify the unique difficulties in training large quantum models, which could
alter the course of quantum machine learning.
|
[
"quant-ph",
"cs.LG"
] | false |
2305.13396
|
2023-05-22T18:17:07Z
|
Developmental Curiosity and Social Interaction in Virtual Agents
|
[
"Chris Doyle",
"Sarah Shader",
"Michelle Lau",
"Megumi Sano",
"Daniel L. K. Yamins",
"Nick Haber"
] |
Infants explore their complex physical and social environment in an organized
way. To gain insight into what intrinsic motivations may help structure this
exploration, we create a virtual infant agent and place it in a
developmentally-inspired 3D environment with no external rewards. The
environment has a virtual caregiver agent with the capability to interact
contingently with the infant agent in ways that resemble play. We test
intrinsic reward functions that are similar to motivations that have been
proposed to drive exploration in humans: surprise, uncertainty, novelty, and
learning progress. These generic reward functions lead the infant agent to
explore its environment and discover the contingencies that are embedded into
the caregiver agent. The reward functions that are proxies for novelty and
uncertainty are the most successful in generating diverse experiences and
activating the environment contingencies. We also find that learning a world
model in the presence of an attentive caregiver helps the infant agent learn
how to predict scenarios with challenging social and physical dynamics. Taken
together, our findings provide insight into how curiosity-like intrinsic
rewards and contingent social interaction lead to dynamic social behavior and
the creation of a robust predictive world model.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.13440
|
2023-05-22T19:30:20Z
|
Differentially Private Medians and Interior Points for Non-Pathological
Data
|
[
"Maryam Aliakbarpour",
"Rose Silver",
"Thomas Steinke",
"Jonathan Ullman"
] |
We construct differentially private estimators with low sample complexity
that estimate the median of an arbitrary distribution over $\mathbb{R}$
satisfying very mild moment conditions. Our result stands in contrast to the
surprising negative result of Bun et al. (FOCS 2015) that showed there is no
differentially private estimator with any finite sample complexity that returns
any non-trivial approximation to the median of an arbitrary distribution.
|
[
"cs.DS",
"cs.LG"
] | false |
2305.13485
|
2023-05-22T20:58:15Z
|
Advancing Community Engaged Approaches to Identifying Structural Drivers
of Racial Bias in Health Diagnostic Algorithms
|
[
"Jill A. Kuhlberg",
"Irene Headen",
"Ellis A. Ballard",
"Donald Martin Jr."
] |
Much attention and concern has been raised recently about bias and the use of
machine learning algorithms in healthcare, especially as it relates to
perpetuating racial discrimination and health disparities. Following an initial
system dynamics workshop at the Data for Black Lives II conference hosted at
MIT in January of 2019, a group of conference participants interested in
building capabilities to use system dynamics to understand complex societal
issues convened monthly to explore issues related to racial bias in AI and
implications for health disparities through qualitative and simulation
modeling. In this paper we present results and insights from the modeling
process and highlight the importance of centering the discussion of data and
healthcare on people and their experiences with healthcare and science, and
recognizing the societal context where the algorithm is operating. Collective
memory of community trauma, through deaths attributed to poor healthcare, and
negative experiences with healthcare are endogenous drivers of seeking
treatment and experiencing effective care, which impact the availability and
quality of data for algorithms. These drivers have drastically disparate
initial conditions for different racial groups and point to limited impact of
focusing solely on improving diagnostic algorithms for achieving better health
outcomes for some groups.
|
[
"cs.LG",
"cs.CY"
] | false |
2305.13546
|
2023-05-22T23:38:27Z
|
Neural Functional Transformers
|
[
"Allan Zhou",
"Kaien Yang",
"Yiding Jiang",
"Kaylee Burns",
"Winnie Xu",
"Samuel Sokota",
"J. Zico Kolter",
"Chelsea Finn"
] |
The recent success of neural networks as implicit representation of data has
driven growing interest in neural functionals: models that can process other
neural networks as input by operating directly over their weight spaces.
Nevertheless, constructing expressive and efficient neural functional
architectures that can handle high-dimensional weight-space objects remains
challenging. This paper uses the attention mechanism to define a novel set of
permutation equivariant weight-space layers and composes them into deep
equivariant models called neural functional Transformers (NFTs). NFTs respect
weight-space permutation symmetries while incorporating the advantages of
attention, which have exhibited remarkable success across multiple domains. In
experiments processing the weights of feedforward MLPs and CNNs, we find that
NFTs match or exceed the performance of prior weight-space methods. We also
leverage NFTs to develop Inr2Array, a novel method for computing permutation
invariant latent representations from the weights of implicit neural
representations (INRs). Our proposed method improves INR classification
accuracy by up to $+17\%$ over existing methods. We provide an implementation
of our layers at https://github.com/AllanYangZhou/nfn.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.13997
|
2023-05-22T13:49:49Z
|
Learning Subpocket Prototypes for Generalizable Structure-based Drug
Design
|
[
"Zaixi Zhang",
"Qi Liu"
] |
Generating molecules with high binding affinities to target proteins (a.k.a.
structure-based drug design) is a fundamental and challenging task in drug
discovery. Recently, deep generative models have achieved remarkable success in
generating 3D molecules conditioned on the protein pocket. However, most
existing methods consider molecular generation for protein pockets
independently while neglecting the underlying connections such as
subpocket-level similarities. Subpockets are the local protein environments of
ligand fragments and pockets with similar subpockets may bind the same
molecular fragment (motif) even though their overall structures are different.
Therefore, the trained models can hardly generalize to unseen protein pockets
in real-world applications. In this paper, we propose a novel method DrugGPS
for generalizable structure-based drug design. With the biochemical priors, we
propose to learn subpocket prototypes and construct a global interaction graph
to model the interactions between subpocket prototypes and molecular motifs.
Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule
generation scheme are used to improve the model's performance. The experimental
results show that our model consistently outperforms baselines in generating
realistic drug candidates with high affinities in challenging
out-of-distribution settings.
|
[
"q-bio.BM",
"cs.LG"
] | false |
2305.16332
|
2023-05-22T01:14:46Z
|
Continual Learning through Human-Robot Interaction -- Human Perceptions
of a Continual Learning Robot in Repeated Interactions
|
[
"Ali Ayub",
"Zachary De Francesco",
"Patrick Holthaus",
"Chrystopher L. Nehaniv",
"Kerstin Dautenhahn"
] |
For long-term deployment in dynamic real-world environments, assistive robots
must continue to learn and adapt to their environments. Researchers have
developed various computational models for continual learning (CL) that can
allow robots to continually learn from limited training data, and avoid
forgetting previous knowledge. While these CL models can mitigate forgetting on
static, systematically collected datasets, it is unclear how human users might
perceive a robot that continually learns over multiple interactions with them.
In this paper, we developed a system that integrates CL models for object
recognition with a Fetch mobile manipulator robot and allows human participants
to directly teach and test the robot over multiple sessions. We conducted an
in-person study with 60 participants who interacted with our system in 300
sessions (5 sessions per participant). We conducted a between-participant study
with three different CL models (3 experimental conditions) to understand human
perceptions of continual learning robots over multiple sessions. Our results
suggest that participants' perceptions of trust, competence, and usability of a
continual learning robot significantly decrease over multiple sessions if the
robot forgets previously learned objects. However, the perceived task load on
participants for teaching and testing the robot remains the same over multiple
sessions even if the robot forgets previously learned objects. Our results also
indicate that state-of-the-art CL models might perform unreliably when applied
to robots interacting with human participants. Further, continual learning
robots are not perceived as very trustworthy or competent by human
participants, regardless of the underlying continual learning model or the
session number.
|
[
"cs.RO",
"cs.LG"
] | false |
2305.18238
|
2023-05-22T15:57:32Z
|
Multi-behavior Self-supervised Learning for Recommendation
|
[
"Jingcao Xu",
"Chaokun Wang",
"Cheng Wu",
"Yang Song",
"Kai Zheng",
"Xiaowei Wang",
"Changping Wang",
"Guorui Zhou",
"Kun Gai"
] |
Modern recommender systems often deal with a variety of user interactions,
e.g., click, forward, purchase, etc., which requires the underlying recommender
engines to fully understand and leverage multi-behavior data from users.
Despite recent efforts towards making use of heterogeneous data, multi-behavior
recommendation still faces great challenges. Firstly, sparse target signals and
noisy auxiliary interactions remain an issue. Secondly, existing methods
utilizing self-supervised learning (SSL) to tackle the data sparsity neglect
the serious optimization imbalance between the SSL task and the target task.
Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework
together with an adaptive optimization method. Specifically, we devise a
behavior-aware graph neural network incorporating the self-attention mechanism
to capture behavior multiplicity and dependencies. To increase the robustness
to data sparsity under the target behavior and noisy interactions from
auxiliary behaviors, we propose a novel self-supervised learning paradigm to
conduct node self-discrimination at both inter-behavior and intra-behavior
levels. In addition, we develop a customized optimization strategy through
hybrid manipulation on gradients to adaptively balance the self-supervised
learning task and the main supervised recommendation task. Extensive
experiments on five real-world datasets demonstrate the consistent improvements
obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our
model implementation at: https://github.com/Scofield666/MBSSL.git.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.18622
|
2023-05-22T15:36:10Z
|
Instant Representation Learning for Recommendation over Large Dynamic
Graphs
|
[
"Cheng Wu",
"Chaokun Wang",
"Jingcao Xu",
"Ziwei Fang",
"Tiankai Gu",
"Changping Wang",
"Yang Song",
"Kai Zheng",
"Xiaowei Wang",
"Guorui Zhou"
] |
Recommender systems are able to learn user preferences based on user and item
representations via their historical behaviors. To improve representation
learning, recent recommendation models start leveraging information from
various behavior types exhibited by users. In real-world scenarios, the user
behavioral graph is not only multiplex but also dynamic, i.e., the graph
evolves rapidly over time, with various types of nodes and edges added or
deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing
methods neglect such streaming dynamics and thus need to be retrained once the
graph has significantly evolved, making them unsuitable in the online learning
environment. Furthermore, the Neighborhood Disturbance existing in dynamic
graphs deteriorates the performance of neighbor-aggregation based graph models.
To this end, we propose SUPA, a novel graph neural network for dynamic
multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture,
SUPA develops a sample-update-propagate architecture to alleviate neighborhood
disturbance. Specifically, for each new edge, SUPA samples an influenced
subgraph, updates the representations of the two interactive nodes, and
propagates the interaction information to the sampled subgraph. Furthermore, to
train SUPA incrementally online, we propose InsLearn, an efficient workflow for
single-pass training of large dynamic graphs. Extensive experimental results on
six real-world datasets show that SUPA has a good generalization ability and is
superior to sixteen state-of-the-art baseline methods. The source code is
available at https://github.com/shatter15/SUPA.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.12618
|
2023-05-22T00:56:00Z
|
Atomic and Subgraph-aware Bilateral Aggregation for Molecular
Representation Learning
|
[
"Jiahao Chen",
"Yurou Liu",
"Jiangmeng Li",
"Bing Su",
"Jirong Wen"
] |
Molecular representation learning is a crucial task in predicting molecular
properties. Molecules are often modeled as graphs where atoms and chemical
bonds are represented as nodes and edges, respectively, and Graph Neural
Networks (GNNs) have been commonly utilized to predict atom-related properties,
such as reactivity and solubility. However, functional groups (subgraphs) are
closely related to some chemical properties of molecules, such as efficacy, and
metabolic properties, which cannot be solely determined by individual atoms. In
this paper, we introduce a new model for molecular representation learning
called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA), which
addresses the limitations of previous atom-wise and subgraph-wise models by
incorporating both types of information. ASBA consists of two branches, one for
atom-wise information and the other for subgraph-wise information. Considering
existing atom-wise GNNs cannot properly extract invariant subgraph features, we
propose a decomposition-polymerization GNN architecture for the subgraph-wise
branch. Furthermore, we propose cooperative node-level and graph-level
self-supervised learning strategies for ASBA to improve its generalization. Our
method offers a more comprehensive way to learn representations for molecular
property prediction and has broad potential in drug and material discovery
applications. Extensive experiments have demonstrated the effectiveness of our
method.
|
[
"cs.LG",
"cs.AI",
"q-bio.QM"
] | false |
2305.12625
|
2023-05-22T01:32:17Z
|
Multirotor Ensemble Model Predictive Control I: Simulation Experiments
|
[
"Erina Yamaguchi",
"Sai Ravela"
] |
Nonlinear receding horizon model predictive control is a powerful approach to
controlling nonlinear dynamical systems. However, typical approaches that use
the Jacobian, adjoint, and forward-backward passes may lose fidelity and
efficacy for highly nonlinear problems. Here, we develop an Ensemble Model
Predictive Control (EMPC) approach wherein the forward model remains fully
nonlinear, and an ensemble-represented Gaussian process performs the backward
calculations to determine optimal gains for the initial time. EMPC admits black
box, possible non-differentiable models, simulations are executable in parallel
over long horizons, and control is uncertainty quantifying and applicable to
stochastic settings. We construct the EMPC for terminal control and regulation
problems and apply it to the control of a quadrotor in a simulated,
identical-twin study. Results suggest that the easily implemented approach is
promising and amenable to controlling autonomous robotic systems with added
state/parameter estimation and parallel computing.
|
[
"eess.SY",
"cs.LG",
"cs.RO",
"cs.SY",
"93B45",
"I.2.9"
] | false |
2305.12640
|
2023-05-22T02:26:29Z
|
Limited Resource Allocation in a Non-Markovian World: The Case of
Maternal and Child Healthcare
|
[
"Panayiotis Danassis",
"Shresth Verma",
"Jackson A. Killian",
"Aparna Taneja",
"Milind Tambe"
] |
The success of many healthcare programs depends on participants' adherence.
We consider the problem of scheduling interventions in low resource settings
(e.g., placing timely support calls from health workers) to increase adherence
and/or engagement. Past works have successfully developed several classes of
Restless Multi-armed Bandit (RMAB) based solutions for this problem.
Nevertheless, all past RMAB approaches assume that the participants' behaviour
follows the Markov property. We demonstrate significant deviations from the
Markov assumption on real-world data on a maternal health awareness program
from our partner NGO, ARMMAN. Moreover, we extend RMABs to continuous state
spaces, a previously understudied area. To tackle the generalised non-Markovian
RMAB setting we (i) model each participant's trajectory as a time-series, (ii)
leverage the power of time-series forecasting models to learn complex patterns
and dynamics to predict future states, and (iii) propose the Time-series Arm
Ranking Index (TARI) policy, a novel algorithm that selects the RMAB arms that
will benefit the most from an intervention, given our future state predictions.
We evaluate our approach on both synthetic data, and a secondary analysis on
real data from ARMMAN, and demonstrate significant increase in engagement
compared to the SOTA, deployed Whittle index solution. This translates to 16.3
hours of additional content listened, 90.8% more engagement drops prevented,
and reaching more than twice as many high dropout-risk beneficiaries.
|
[
"cs.AI",
"cs.LG",
"stat.ML"
] | false |
2305.12703
|
2023-05-22T04:26:18Z
|
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain
Adaptation Speaker Verification
|
[
"Zhuo Li",
"Jingze Lu",
"Zhenduo Zhao",
"Wenchao Wang",
"Pengyuan Zhang"
] |
Utilizing the large-scale unlabeled data from the target domain via
pseudo-label clustering algorithms is an important approach for addressing
domain adaptation problems in speaker verification tasks. In this paper, we
propose a novel progressive subgraph clustering algorithm based on multi-model
voting and double-Gaussian based assessment (PGMVG clustering). To fully
exploit the relationships among utterances and the complementarity among
multiple models, our method constructs multiple k-nearest neighbors graphs
based on diverse models and generates high-confidence edges using a voting
mechanism. Further, to maximize the intra-class diversity, the connected
subgraph is utilized to obtain the initial pseudo-labels. Finally, to prevent
disastrous clustering results, we adopt an iterative approach that
progressively increases k and employs a double-Gaussian based assessment
algorithm to decide whether merging sub-classes.
|
[
"cs.SD",
"cs.LG",
"eess.AS"
] | false |
2305.12738
|
2023-05-22T05:59:22Z
|
Logical Entity Representation in Knowledge-Graphs for Differentiable
Rule Learning
|
[
"Chi Han",
"Qizheng He",
"Charles Yu",
"Xinya Du",
"Hanghang Tong",
"Heng Ji"
] |
Probabilistic logical rule learning has shown great strength in logical rule
mining and knowledge graph completion. It learns logical rules to predict
missing edges by reasoning on existing edges in the knowledge graph. However,
previous efforts have largely been limited to only modeling chain-like Horn
clauses such as $R_1(x,z)\land R_2(z,y)\Rightarrow H(x,y)$. This formulation
overlooks additional contextual information from neighboring sub-graphs of
entity variables $x$, $y$ and $z$. Intuitively, there is a large gap here, as
local sub-graphs have been found to provide important information for knowledge
graph completion. Inspired by these observations, we propose Logical Entity
RePresentation (LERP) to encode contextual information of entities in the
knowledge graph. A LERP is designed as a vector of probabilistic logical
functions on the entity's neighboring sub-graph. It is an interpretable
representation while allowing for differentiable optimization. We can then
incorporate LERP into probabilistic logical rule learning to learn more
expressive rules. Empirical results demonstrate that with LERP, our model
outperforms other rule learning methods in knowledge graph completion and is
comparable or even superior to state-of-the-art black-box methods. Moreover, we
find that our model can discover a more expressive family of logical rules.
LERP can also be further combined with embedding learning methods like TransE
to make it more interpretable.
|
[
"cs.AI",
"cs.LG",
"cs.LO"
] | false |
2305.12741
|
2023-05-22T06:09:10Z
|
Coswara: A respiratory sounds and symptoms dataset for remote screening
of SARS-CoV-2 infection
|
[
"Debarpan Bhattacharya",
"Neeraj Kumar Sharma",
"Debottam Dutta",
"Srikanth Raj Chetupalli",
"Pravin Mote",
"Sriram Ganapathy",
"Chandrakiran C",
"Sahiti Nori",
"Suhail K K",
"Sadhana Gonuguntla",
"Murali Alagesan"
] |
This paper presents the Coswara dataset, a dataset containing diverse set of
respiratory sounds and rich meta-data, recorded between April-2020 and
February-2022 from 2635 individuals (1819 SARS-CoV-2 negative, 674 positive,
and 142 recovered subjects). The respiratory sounds contained nine sound
categories associated with variants of breathing, cough and speech. The rich
metadata contained demographic information associated with age, gender and
geographic location, as well as the health information relating to the
symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test
status. Our study is the first of its kind to manually annotate the audio
quality of the entire dataset (amounting to 65~hours) through manual listening.
The paper summarizes the data collection procedure, demographic, symptoms and
audio data information. A COVID-19 classifier based on bi-directional long
short-term (BLSTM) architecture, is trained and evaluated on the different
population sub-groups contained in the dataset to understand the bias/fairness
of the model. This enabled the analysis of the impact of gender, geographic
location, date of recording, and language proficiency on the COVID-19 detection
performance.
|
[
"eess.AS",
"cs.LG",
"cs.SD",
"q-bio.QM"
] | false |
2305.12768
|
2023-05-22T06:55:38Z
|
uCTRL: Unbiased Contrastive Representation Learning via Alignment and
Uniformity for Collaborative Filtering
|
[
"Jae-woong Lee",
"Seongmin Park",
"Mincheol Yoon",
"Jongwuk Lee"
] |
Because implicit user feedback for the collaborative filtering (CF) models is
biased toward popular items, CF models tend to yield recommendation lists with
popularity bias. Previous studies have utilized inverse propensity weighting
(IPW) or causal inference to mitigate this problem. However, they solely employ
pointwise or pairwise loss functions and neglect to adopt a contrastive loss
function for learning meaningful user and item representations. In this paper,
we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing
alignment and uniformity functions derived from the InfoNCE loss function for
CF models. Specifically, we formulate an unbiased alignment function used in
uCTRL. We also devise a novel IPW estimation method that removes the bias of
both users and items. Despite its simplicity, uCTRL equipped with existing CF
models consistently outperforms state-of-the-art unbiased recommender models,
up to 12.22% for Recall@20 and 16.33% for NDCG@20 gains, on four benchmark
datasets.
|
[
"cs.IR",
"cs.AI",
"cs.LG"
] | false |
2305.12821
|
2023-05-22T08:29:00Z
|
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon
Complex Manipulation
|
[
"Minho Heo",
"Youngwoon Lee",
"Doohyun Lee",
"Joseph J. Lim"
] |
Reinforcement learning (RL), imitation learning (IL), and task and motion
planning (TAMP) have demonstrated impressive performance across various robotic
manipulation tasks. However, these approaches have been limited to learning
simple behaviors in current real-world manipulation benchmarks, such as pushing
or pick-and-place. To enable more complex, long-horizon behaviors of an
autonomous robot, we propose to focus on real-world furniture assembly, a
complex, long-horizon robot manipulation task that requires addressing many
current robotic manipulation challenges to solve. We present FurnitureBench, a
reproducible real-world furniture assembly benchmark aimed at providing a low
barrier for entry and being easily reproducible, so that researchers across the
world can reliably test their algorithms and compare them against prior work.
For ease of use, we provide 200+ hours of pre-collected data (5000+
demonstrations), 3D printable furniture models, a robotic environment setup
guide, and systematic task initialization. Furthermore, we provide
FurnitureSim, a fast and realistic simulator of FurnitureBench. We benchmark
the performance of offline RL and IL algorithms on our assembly tasks and
demonstrate the need to improve such algorithms to be able to solve our tasks
in the real world, providing ample opportunities for future research.
|
[
"cs.RO",
"cs.AI",
"cs.LG"
] | false |
2305.12886
|
2023-05-22T10:10:23Z
|
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic
Policies
|
[
"Dionis Totsila",
"Konstantinos Chatzilygeroudis",
"Denis Hadjivelichkov",
"Valerio Modugno",
"Ioannis Hatzilygeroudis",
"Dimitrios Kanoulas"
] |
State-of-the-art sensorimotor learning algorithms offer policies that can
often produce unstable behaviors, damaging the robot and/or the environment.
Traditional robot learning, on the contrary, relies on dynamical system-based
policies that can be analyzed for stability/safety. Such policies, however, are
neither flexible nor generic and usually work only with proprioceptive sensor
states. In this work, we bridge the gap between generic neural network policies
and dynamical system-based policies, and we introduce Autonomous Neural Dynamic
Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b)
always produce asymptotically stable behaviors, and (c) are more flexible than
traditional stable dynamical system-based policies. ANDPs are fully
differentiable, flexible generic-policies that can be used in imitation
learning setups while ensuring asymptotic stability. In this paper, we explore
the flexibility and capacity of ANDPs in several imitation learning tasks
including experiments with image observations. The results show that ANDPs
combine the benefits of both neural network-based and dynamical system-based
methods.
|
[
"cs.RO",
"cs.AI",
"cs.LG",
"math.OC"
] | false |
2305.12887
|
2023-05-22T10:10:35Z
|
ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text
and Speech using Adversarial Disentanglement of Multimodal Style Encoding
|
[
"Mireille Fares",
"Catherine Pelachaud",
"Nicolas Obin"
] |
In this study, we address the importance of modeling behavior style in
virtual agents for personalized human-agent interaction. We propose a machine
learning approach to synthesize gestures, driven by prosodic features and text,
in the style of different speakers, even those unseen during training. Our
model incorporates zero-shot multimodal style transfer using multimodal data
from the PATS database, which contains videos of diverse speakers. We recognize
style as a pervasive element during speech, influencing the expressivity of
communicative behaviors, while content is conveyed through multimodal signals
and text. By disentangling content and style, we directly infer the style
embedding, even for speakers not included in the training phase, without the
need for additional training or fine-tuning. Objective and subjective
evaluations are conducted to validate our approach and compare it against two
baseline methods.
|
[
"eess.AS",
"cs.AI",
"cs.LG",
"cs.SD"
] | false |
2305.12892
|
2023-05-22T10:20:34Z
|
Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex
models for robot navigation and environment pseudo-mapping
|
[
"Daniel Casanueva-Morato",
"Alvaro Ayuso-Martinez",
"Juan P. Dominguez-Morales",
"Angel Jimenez-Fernandez",
"Gabriel Jimenez-Moreno",
"Fernando Perez-Pena"
] |
The brain has a great capacity for computation and efficient resolution of
complex problems, far surpassing modern computers. Neuromorphic engineering
seeks to mimic the basic principles of the brain to develop systems capable of
achieving such capabilities. In the neuromorphic field, navigation systems are
of great interest due to their potential applicability to robotics, although
these systems are still a challenge to be solved. This work proposes a
spike-based robotic navigation and environment pseudomapping system formed by a
bio-inspired hippocampal memory model connected to a Posterior Parietal Cortex
model. The hippocampus is in charge of maintaining a representation of an
environment state map, and the PPC is in charge of local decision-making. This
system was implemented on the SpiNNaker hardware platform using Spiking Neural
Networks. A set of real-time experiments was applied to demonstrate the correct
functioning of the system in virtual and physical environments on a robotic
platform. The system is able to navigate through the environment to reach a
goal position starting from an initial position, avoiding obstacles and mapping
the environment. To the best of the authors knowledge, this is the first
implementation of an environment pseudo-mapping system with dynamic learning
based on a bio-inspired hippocampal memory.
|
[
"cs.RO",
"cs.LG",
"cs.NE"
] | false |
2305.12914
|
2023-05-22T10:55:01Z
|
IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin
Machines
|
[
"Omar Ghazal",
"Simranjeet Singh",
"Tousif Rahman",
"Shengqi Yu",
"Yujin Zheng",
"Domenico Balsamo",
"Sachin Patkar",
"Farhad Merchant",
"Fei Xia",
"Alex Yakovlev",
"Rishad Shafik"
] |
In-memory computing for Machine Learning (ML) applications remedies the von
Neumann bottlenecks by organizing computation to exploit parallelism and
locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer
integrated switching and storage capabilities showing promising performance for
ML applications. However, ReRAM devices have design challenges, such as
non-linear digital-analog conversion and circuit overheads. This paper proposes
an In-Memory Boolean-to-Current Inference Architecture (IMBUE) that uses
ReRAM-transistor cells to eliminate the need for such conversions. IMBUE
processes Boolean feature inputs expressed as digital voltages and generates
parallel current paths based on resistive memory states. The proportional
column current is then translated back to the Boolean domain for further
digital processing. The IMBUE architecture is inspired by the Tsetlin Machine
(TM), an emerging ML algorithm based on intrinsically Boolean logic. The IMBUE
architecture demonstrates significant performance improvements over binarized
convolutional neural networks and digital TM in-memory implementations,
achieving up to a 12.99x and 5.28x increase, respectively.
|
[
"cs.AR",
"cs.AI",
"cs.ET",
"cs.LG"
] | false |
2305.12935
|
2023-05-22T11:30:00Z
|
CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities
|
[
"Yisheng Alison Zheng",
"Abdallah Lakhdari",
"Amani Abusafia",
"Shing Tai Tony Lui",
"Athman Bouguettaya"
] |
Human mobility patterns refer to the regularities and trends in the way
people move, travel, or navigate through different geographical locations over
time. Detecting human mobility patterns is essential for a variety of
applications, including smart cities, transportation management, and disaster
response. The accuracy of current mobility prediction models is less than 25%.
The low accuracy is mainly due to the fluid nature of human movement.
Typically, humans do not adhere to rigid patterns in their daily activities,
making it difficult to identify hidden regularities in their data. To address
this issue, we proposed a web platform to visualize human mobility patterns by
abstracting the locations into a set of places to detect more realistic
patterns. However, the platform was initially designed to detect individual
mobility patterns, making it unsuitable for representing the crowd in a smart
city scale. Therefore, we extend the platform to visualize the mobility of
multiple users from a city-scale perspective. Our platform allows users to
visualize a graph of visited places based on their historical records using a
modified PrefixSpan approach. Additionally, the platform synchronizes,
aggregates, and displays crowd mobility patterns across various time intervals
within a smart city. We showcase our platform using a real dataset.
|
[
"cs.SI",
"cs.DM",
"cs.HC",
"cs.LG"
] | false |
2305.13041
|
2023-05-22T13:48:30Z
|
Distributed Learning over Networks with Graph-Attention-Based
Personalization
|
[
"Zhuojun Tian",
"Zhaoyang Zhang",
"Zhaohui Yang",
"Richeng Jin",
"Huaiyu Dai"
] |
In conventional distributed learning over a network, multiple agents
collaboratively build a common machine learning model. However, due to the
underlying non-i.i.d. data distribution among agents, the unified learning
model becomes inefficient for each agent to process its locally accessible
data. To address this problem, we propose a graph-attention-based personalized
training algorithm (GATTA) for distributed deep learning. The GATTA enables
each agent to train its local personalized model while exploiting its
correlation with neighboring nodes and utilizing their useful information for
aggregation. In particular, the personalized model in each agent is composed of
a global part and a node-specific part. By treating each agent as one node in a
graph and the node-specific parameters as its features, the benefits of the
graph attention mechanism can be inherited. Namely, instead of aggregation
based on averaging, it learns the specific weights for different neighboring
nodes without requiring prior knowledge about the graph structure or the
neighboring nodes' data distribution. Furthermore, relying on the
weight-learning procedure, we develop a communication-efficient GATTA by
skipping the transmission of information with small aggregation weights.
Additionally, we theoretically analyze the convergence properties of GATTA for
non-convex loss functions. Numerical results validate the excellent
performances of the proposed algorithms in terms of convergence and
communication cost.
|
[
"cs.DC",
"cs.LG",
"eess.SP"
] | false |
2305.13043
|
2023-05-22T13:48:46Z
|
Self-Replication, Spontaneous Mutations, and Exponential Genetic Drift
in Neural Cellular Automata
|
[
"Lana Sinapayen"
] |
This paper reports on patterns exhibiting self-replication with spontaneous,
inheritable mutations and exponential genetic drift in Neural Cellular
Automata. Despite the models not being explicitly trained for mutation or
inheritability, the descendant patterns exponentially drift away from ancestral
patterns, even when the automaton is deterministic. While this is far from
being the first instance of evolutionary dynamics in a cellular automaton, it
is the first to do so by exploiting the power and convenience of Neural
Cellular Automata, arguably increasing the space of variations and the
opportunity for Open Ended Evolution.
|
[
"cs.NE",
"cs.LG",
"q-bio.PE"
] | false |
2305.13064
|
2023-05-22T14:27:27Z
|
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow
Solutions in Scalar Networks and Beyond
|
[
"Itai Kreisler",
"Mor Shpigel Nacson",
"Daniel Soudry",
"Yair Carmon"
] |
Recent research shows that when Gradient Descent (GD) is applied to neural
networks, the loss almost never decreases monotonically. Instead, the loss
oscillates as gradient descent converges to its ''Edge of Stability'' (EoS).
Here, we find a quantity that does decrease monotonically throughout GD
training: the sharpness attained by the gradient flow solution (GFS)-the
solution that would be obtained if, from now until convergence, we train with
an infinitesimal step size. Theoretically, we analyze scalar neural networks
with the squared loss, perhaps the simplest setting where the EoS phenomena
still occur. In this model, we prove that the GFS sharpness decreases
monotonically. Using this result, we characterize settings where GD provably
converges to the EoS in scalar networks. Empirically, we show that GD
monotonically decreases the GFS sharpness in a squared regression model as well
as practical neural network architectures.
|
[
"cs.LG",
"math.OC",
"stat.ML"
] | false |
2305.13078
|
2023-05-22T14:48:58Z
|
Optimality Principles in Spacecraft Neural Guidance and Control
|
[
"Dario Izzo",
"Emmanuel Blazquez",
"Robin Ferede",
"Sebastien Origer",
"Christophe De Wagter",
"Guido C. H. E. de Croon"
] |
Spacecraft and drones aimed at exploring our solar system are designed to
operate in conditions where the smart use of onboard resources is vital to the
success or failure of the mission. Sensorimotor actions are thus often derived
from high-level, quantifiable, optimality principles assigned to each task,
utilizing consolidated tools in optimal control theory. The planned actions are
derived on the ground and transferred onboard where controllers have the task
of tracking the uploaded guidance profile. Here we argue that end-to-end neural
guidance and control architectures (here called G&CNets) allow transferring
onboard the burden of acting upon these optimality principles. In this way, the
sensor information is transformed in real time into optimal plans thus
increasing the mission autonomy and robustness. We discuss the main results
obtained in training such neural architectures in simulation for interplanetary
transfers, landings and close proximity operations, highlighting the successful
learning of optimality principles by the neural model. We then suggest drone
racing as an ideal gym environment to test these architectures on real robotic
platforms, thus increasing confidence in their utilization on future space
exploration missions. Drone racing shares with spacecraft missions both limited
onboard computational capabilities and similar control structures induced from
the optimality principle sought, but it also entails different levels of
uncertainties and unmodelled effects. Furthermore, the success of G&CNets on
extremely resource-restricted drones illustrates their potential to bring
real-time optimal control within reach of a wider variety of robotic systems,
both in space and on Earth.
|
[
"cs.RO",
"astro-ph.EP",
"cs.LG"
] | false |
2305.13209
|
2023-05-22T16:43:36Z
|
Faster Differentially Private Convex Optimization via Second-Order
Methods
|
[
"Arun Ganesh",
"Mahdi Haghifam",
"Thomas Steinke",
"Abhradeep Thakurta"
] |
Differentially private (stochastic) gradient descent is the workhorse of DP
private machine learning in both the convex and non-convex settings. Without
privacy constraints, second-order methods, like Newton's method, converge
faster than first-order methods like gradient descent. In this work, we
investigate the prospect of using the second-order information from the loss
function to accelerate DP convex optimization. We first develop a private
variant of the regularized cubic Newton method of Nesterov and Polyak, and show
that for the class of strongly convex loss functions, our algorithm has
quadratic convergence and achieves the optimal excess loss. We then design a
practical second-order DP algorithm for the unconstrained logistic regression
problem. We theoretically and empirically study the performance of our
algorithm. Empirical results show our algorithm consistently achieves the best
excess loss compared to other baselines and is 10-40x faster than DP-GD/DP-SGD.
|
[
"cs.LG",
"cs.CR",
"math.OC",
"stat.ML"
] | false |
2305.13215
|
2023-05-22T16:46:37Z
|
Sequence-to-Sequence Forecasting-aided State Estimation for Power
Systems
|
[
"Kamal Basulaiman",
"Masoud Barati"
] |
Power system state forecasting has gained more attention in real-time
operations recently. Unique challenges to energy systems are emerging with the
massive deployment of renewable energy resources. As a result, power system
state forecasting are becoming more crucial for monitoring, operating and
securing modern power systems. This paper proposes an end-to-end deep learning
framework to accurately predict multi-step power system state estimations in
real-time. In our model, we employ a sequence-to-sequence framework to allow
for multi-step forecasting. Bidirectional gated recurrent units (BiGRUs) are
incorporated into the model to achieve high prediction accuracy. The dominant
performance of our model is validated using real dataset. Experimental results
show the superiority of our model in predictive power compared to existing
alternatives.
|
[
"eess.SY",
"cs.LG",
"cs.SY"
] | false |
2305.13262
|
2023-05-22T17:33:07Z
|
Modulation Extraction for LFO-driven Audio Effects
|
[
"Christopher Mitcheltree",
"Christian J. Steinmetz",
"Marco Comunità",
"Joshua D. Reiss"
] |
Low frequency oscillator (LFO) driven audio effects such as phaser, flanger,
and chorus, modify an input signal using time-varying filters and delays,
resulting in characteristic sweeping or widening effects. It has been shown
that these effects can be modeled using neural networks when conditioned with
the ground truth LFO signal. However, in most cases, the LFO signal is not
accessible and measurement from the audio signal is nontrivial, hindering the
modeling process. To address this, we propose a framework capable of extracting
arbitrary LFO signals from processed audio across multiple digital audio
effects, parameter settings, and instrument configurations. Since our system
imposes no restrictions on the LFO signal shape, we demonstrate its ability to
extract quasiperiodic, combined, and distorted modulation signals that are
relevant to effect modeling. Furthermore, we show how coupling the extraction
model with a simple processing network enables training of end-to-end black-box
models of unseen analog or digital LFO-driven audio effects using only dry and
wet audio pairs, overcoming the need to access the audio effect or internal LFO
signal. We make our code available and provide the trained audio effect models
in a real-time VST plugin.
|
[
"cs.SD",
"cs.LG",
"eess.AS"
] | false |
2305.13350
|
2023-05-22T17:14:45Z
|
A Multiple Parameter Linear Scale-Space for one dimensional Signal
Classification
|
[
"Leon A. Luxemburg",
"Steven B. Damelin"
] |
In this article we construct a maximal set of kernels for a multi-parameter
linear scale-space that allow us to construct trees for classification and
recognition of one-dimensional continuous signals similar the Gaussian linear
scale-space approach. Fourier transform formulas are provided and used for
quick and efficient computations. A number of useful properties of the maximal
set of kernels are derived. We also strengthen and generalize some previous
results on the classification of Gaussian kernels. Finally, a new topologically
invariant method of constructing trees is introduced.
|
[
"math.ST",
"cs.LG",
"stat.TH",
"42A63, 42A16, 42A20, 94A12"
] | false |
2305.13472
|
2023-05-22T20:33:29Z
|
A comprehensive theoretical framework for the optimization of neural
networks classification performance with respect to weighted metrics
|
[
"Francesco Marchetti",
"Sabrina Guastavino",
"Cristina Campi",
"Federico Benvenuto",
"Michele Piana"
] |
In many contexts, customized and weighted classification scores are designed
in order to evaluate the goodness of the predictions carried out by neural
networks. However, there exists a discrepancy between the maximization of such
scores and the minimization of the loss function in the training phase. In this
paper, we provide a complete theoretical setting that formalizes weighted
classification metrics and then allows the construction of losses that drive
the model to optimize these metrics of interest. After a detailed theoretical
analysis, we show that our framework includes as particular instances
well-established approaches such as classical cost-sensitive learning, weighted
cross entropy loss functions and value-weighted skill scores.
|
[
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | false |
2305.18321
|
2023-05-22T15:40:01Z
|
Training an Ising Machine with Equilibrium Propagation
|
[
"Jérémie Laydevant",
"Danijela Markovic",
"Julie Grollier"
] |
Ising machines, which are hardware implementations of the Ising model of
coupled spins, have been influential in the development of unsupervised
learning algorithms at the origins of Artificial Intelligence (AI). However,
their application to AI has been limited due to the complexities in matching
supervised training methods with Ising machine physics, even though these
methods are essential for achieving high accuracy. In this study, we
demonstrate a novel approach to train Ising machines in a supervised way
through the Equilibrium Propagation algorithm, achieving comparable results to
software-based implementations. We employ the quantum annealing procedure of
the D-Wave Ising machine to train a fully-connected neural network on the MNIST
dataset. Furthermore, we demonstrate that the machine's connectivity supports
convolution operations, enabling the training of a compact convolutional
network with minimal spins per neuron. Our findings establish Ising machines as
a promising trainable hardware platform for AI, with the potential to enhance
machine learning applications.
|
[
"cs.NE",
"cs.LG",
"quant-ph"
] | false |
2305.12639
|
2023-05-22T02:22:14Z
|
Accelerating Graph Neural Networks via Edge Pruning for Power Allocation
in Wireless Networks
|
[
"Lili Chen",
"Jingge Zhu",
"Jamie Evans"
] |
Neural Networks (GNNs) have recently emerged as a promising approach to
tackling power allocation problems in wireless networks. Since unpaired
transmitters and receivers are often spatially distant, the distanced-based
threshold is proposed to reduce the computation time by excluding or including
the channel state information in GNNs. In this paper, we are the first to
introduce a neighbour-based threshold approach to GNNs to reduce the time
complexity. Furthermore, we conduct a comprehensive analysis of both
distance-based and neighbour-based thresholds and provide recommendations for
selecting the appropriate value in different communication channel scenarios.
We design the corresponding distance-based and neighbour-based Graph Neural
Networks with the aim of allocating transmit powers to maximise the network
throughput. Our results show that our proposed GNNs offer significant
advantages in terms of reducing time complexity while preserving strong
performance. Besides, we show that by choosing a suitable threshold, the time
complexity is reduced from O(|V|^2) to O(|V|), where |V| is the total number of
transceiver pairs.
|
[
"cs.IT",
"cs.LG",
"cs.NI",
"eess.SP",
"math.IT"
] | false |
2305.13570
|
2023-05-23T01:03:23Z
|
Cross-source Point Cloud Registration: Challenges, Progress and
Prospects
|
[
"Xiaoshui Huang",
"Guofeng Mei",
"Jian Zhang"
] |
The emerging topic of cross-source point cloud (CSPC) registration has
attracted increasing attention with the fast development background of 3D
sensor technologies. Different from the conventional same-source point clouds
that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from
different kinds of 3D sensors (e.g., Kinect and { LiDAR}). CSPC registration
generalizes the requirement of data acquisition from same-source to different
sources, which leads to generalized applications and combines the advantages of
multiple sensors. In this paper, we provide a systematic review on CSPC
registration. We first present the characteristics of CSPC, and then summarize
the key challenges in this research area, followed by the corresponding
research progress consisting of the most recent and representative developments
on this topic. Finally, we discuss the important research directions in this
vibrant area and explain the role in several application fields.
|
[
"cs.CV"
] | false |
2305.13579
|
2023-05-23T01:14:53Z
|
Enhancing Detail Preservation for Customized Text-to-Image Generation: A
Regularization-Free Approach
|
[
"Yufan Zhou",
"Ruiyi Zhang",
"Tong Sun",
"Jinhui Xu"
] |
Recent text-to-image generation models have demonstrated impressive
capability of generating text-aligned images with high fidelity. However,
generating images of novel concept provided by the user input image is still a
challenging task. To address this problem, researchers have been exploring
various methods for customizing pre-trained text-to-image generation models.
Currently, most existing methods for customizing pre-trained text-to-image
generation models involve the use of regularization techniques to prevent
over-fitting. While regularization will ease the challenge of customization and
leads to successful content creation with respect to text guidance, it may
restrict the model capability, resulting in the loss of detailed information
and inferior performance. In this work, we propose a novel framework for
customized text-to-image generation without the use of regularization.
Specifically, our proposed framework consists of an encoder network and a novel
sampling method which can tackle the over-fitting problem without the use of
regularization. With the proposed framework, we are able to customize a
large-scale text-to-image generation model within half a minute on single GPU,
with only one image provided by the user. We demonstrate in experiments that
our proposed framework outperforms existing methods, and preserves more
fine-grained details.
|
[
"cs.CV"
] | true |
2305.13593
|
2023-05-23T01:55:37Z
|
Neural Image Re-Exposure
|
[
"Xinyu Zhang",
"Hefei Huang",
"Xu Jia",
"Dong Wang",
"Huchuan Lu"
] |
The shutter strategy applied to the photo-shooting process has a significant
influence on the quality of the captured photograph. An improper shutter may
lead to a blurry image, video discontinuity, or rolling shutter artifact.
Existing works try to provide an independent solution for each issue. In this
work, we aim to re-expose the captured photo in post-processing to provide a
more flexible way of addressing those issues within a unified framework.
Specifically, we propose a neural network-based image re-exposure framework. It
consists of an encoder for visual latent space construction, a re-exposure
module for aggregating information to neural film with a desired shutter
strategy, and a decoder for 'developing' neural film into a desired image. To
compensate for information confusion and missing frames, event streams, which
can capture almost continuous brightness changes, are leveraged in computing
visual latent content. Both self-attention layers and cross-attention layers
are employed in the re-exposure module to promote interaction between neural
film and visual latent content and information aggregation to neural film. The
proposed unified image re-exposure framework is evaluated on several
shutter-related image recovery tasks and performs favorably against independent
state-of-the-art methods.
|
[
"cs.CV"
] | false |
2305.13605
|
2023-05-23T02:14:11Z
|
Adaptive Face Recognition Using Adversarial Information Network
|
[
"Mei Wang",
"Weihong Deng"
] |
In many real-world applications, face recognition models often degenerate
when training data (referred to as source domain) are different from testing
data (referred to as target domain). To alleviate this mismatch caused by some
factors like pose and skin tone, the utilization of pseudo-labels generated by
clustering algorithms is an effective way in unsupervised domain adaptation.
However, they always miss some hard positive samples. Supervision on
pseudo-labeled samples attracts them towards their prototypes and would cause
an intra-domain gap between pseudo-labeled samples and the remaining unlabeled
samples within target domain, which results in the lack of discrimination in
face recognition. In this paper, considering the particularity of face
recognition, we propose a novel adversarial information network (AIN) to
address it. First, a novel adversarial mutual information (MI) loss is proposed
to alternately minimize MI with respect to the target classifier and maximize
MI with respect to the feature extractor. By this min-max manner, the positions
of target prototypes are adaptively modified which makes unlabeled images
clustered more easily such that intra-domain gap can be mitigated. Second, to
assist adversarial MI loss, we utilize a graph convolution network to predict
linkage likelihoods between target data and generate pseudo-labels. It
leverages valuable information in the context of nodes and can achieve more
reliable results. The proposed method is evaluated under two scenarios, i.e.,
domain adaptation across poses and image conditions, and domain adaptation
across faces with different skin tones. Extensive experiments show that AIN
successfully improves cross-domain generalization and offers a new
state-of-the-art on RFW dataset.
|
[
"cs.CV"
] | false |
2305.13607
|
2023-05-23T02:15:53Z
|
Not All Image Regions Matter: Masked Vector Quantization for
Autoregressive Image Generation
|
[
"Mengqi Huang",
"Zhendong Mao",
"Quan Wang",
"Yongdong Zhang"
] |
Existing autoregressive models follow the two-stage generation paradigm that
first learns a codebook in the latent space for image reconstruction and then
completes the image generation autoregressively based on the learned codebook.
However, existing codebook learning simply models all local region information
of images without distinguishing their different perceptual importance, which
brings redundancy in the learned codebook that not only limits the next stage's
autoregressive model's ability to model important structure but also results in
high training cost and slow generation speed. In this study, we borrow the idea
of importance perception from classical image coding theory and propose a novel
two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and
Stackformer, to relieve the model from modeling redundancy. Specifically,
MQ-VAE incorporates an adaptive mask module for masking redundant region
features before quantization and an adaptive de-mask module for recovering the
original grid image feature map to faithfully reconstruct the original images
after quantization. Then, Stackformer learns to predict the combination of the
next code and its position in the feature map. Comprehensive experiments on
various image generation validate our effectiveness and efficiency. Code will
be released at https://github.com/CrossmodalGroup/MaskedVectorQuantization.
|
[
"cs.CV"
] | false |
2305.13611
|
2023-05-23T02:20:12Z
|
A New Comprehensive Benchmark for Semi-supervised Video Anomaly
Detection and Anticipation
|
[
"Congqi Cao",
"Yue Lu",
"Peng Wang",
"Yanning Zhang"
] |
Semi-supervised video anomaly detection (VAD) is a critical task in the
intelligent surveillance system. However, an essential type of anomaly in VAD
named scene-dependent anomaly has not received the attention of researchers.
Moreover, there is no research investigating anomaly anticipation, a more
significant task for preventing the occurrence of anomalous events. To this
end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes,
28 classes of abnormal events, and 16 hours of videos. At present, it is the
largest semi-supervised VAD dataset with the largest number of scenes and
classes of anomalies, the longest duration, and the only one considering the
scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for
video anomaly anticipation. We further propose a novel model capable of
detecting and anticipating anomalous events simultaneously. Compared with 7
outstanding VAD algorithms in recent years, our method can cope with
scene-dependent anomaly detection and anomaly anticipation both well, achieving
state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and
the newly proposed NWPU Campus datasets consistently. Our dataset and code is
available at: https://campusvad.github.io.
|
[
"cs.CV"
] | false |
2305.13620
|
2023-05-23T02:31:06Z
|
A Dive into SAM Prior in Image Restoration
|
[
"Zeyu Xiao",
"Jiawang Bai",
"Zhihe Lu",
"Zhiwei Xiong"
] |
The goal of image restoration (IR), a fundamental issue in computer vision,
is to restore a high-quality (HQ) image from its degraded low-quality (LQ)
observation. Multiple HQ solutions may correspond to an LQ input in this poorly
posed problem, creating an ambiguous solution space. This motivates the
investigation and incorporation of prior knowledge in order to effectively
constrain the solution space and enhance the quality of the restored images. In
spite of the pervasive use of hand-crafted and learned priors in IR, limited
attention has been paid to the incorporation of knowledge from large-scale
foundation models. In this paper, we for the first time leverage the prior
knowledge of the state-of-the-art segment anything model (SAM) to boost the
performance of existing IR networks in an parameter-efficient tuning manner. In
particular, the choice of SAM is based on its robustness to image degradations,
such that HQ semantic masks can be extracted from it. In order to leverage
semantic priors and enhance restoration quality, we propose a lightweight SAM
prior tuning (SPT) unit. This plug-and-play component allows us to effectively
integrate semantic priors into existing IR networks, resulting in significant
improvements in restoration quality. As the only trainable module in our
method, the SPT unit has the potential to improve both efficiency and
scalability. We demonstrate the effectiveness of the proposed method in
enhancing a variety of methods across multiple tasks, such as image
super-resolution and color image denoising.
|
[
"cs.CV"
] | false |
2305.13653
|
2023-05-23T03:53:57Z
|
RaSa: Relation and Sensitivity Aware Representation Learning for
Text-based Person Search
|
[
"Yang Bai",
"Min Cao",
"Daming Gao",
"Ziqiang Cao",
"Chen Chen",
"Zhenfeng Fan",
"Liqiang Nie",
"Min Zhang"
] |
Text-based person search aims to retrieve the specified person images given a
textual description. The key to tackling such a challenging task is to learn
powerful multi-modal representations. Towards this, we propose a Relation and
Sensitivity aware representation learning method (RaSa), including two novel
tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). For
one thing, existing methods cluster representations of all positive pairs
without distinction and overlook the noise problem caused by the weak positive
pairs where the text and the paired image have noise correspondences, thus
leading to overfitting learning. RA offsets the overfitting risk by introducing
a novel positive relation detection task (i.e., learning to distinguish strong
and weak positive pairs). For another thing, learning invariant representation
under data augmentation (i.e., being insensitive to some transformations) is a
general practice for improving representation's robustness in existing methods.
Beyond that, we encourage the representation to perceive the sensitive
transformation by SA (i.e., learning to detect the replaced words), thus
promoting the representation's robustness. Experiments demonstrate that RaSa
outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in
terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively.
Code is available at: https://github.com/Flame-Chasers/RaSa.
|
[
"cs.CV"
] | false |
2305.13704
|
2023-05-23T05:41:53Z
|
FlowChroma -- A Deep Recurrent Neural Network for Video Colorization
|
[
"Thejan Wijesinghe",
"Chamath Abeysinghe",
"Chanuka Wijayakoon",
"Lahiru Jayathilake",
"Uthayasanker Thayasivam"
] |
We develop an automated video colorization framework that minimizes the
flickering of colors across frames. If we apply image colorization techniques
to successive frames of a video, they treat each frame as a separate
colorization task. Thus, they do not necessarily maintain the colors of a scene
consistently across subsequent frames. The proposed solution includes a novel
deep recurrent encoder-decoder architecture which is capable of maintaining
temporal and contextual coherence between consecutive frames of a video. We use
a high-level semantic feature extractor to automatically identify the context
of a scenario including objects, with a custom fusion layer that combines the
spatial and temporal features of a frame sequence. We demonstrate experimental
results, qualitatively showing that recurrent neural networks can be
successfully used to improve color consistency in video colorization.
|
[
"cs.CV"
] | false |
2305.13705
|
2023-05-23T05:44:03Z
|
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models
|
[
"Lijun Li",
"Li'an Zhuo",
"Bang Zhang",
"Liefeng Bo",
"Chen Chen"
] |
Hand mesh reconstruction from the monocular image is a challenging task due
to its depth ambiguity and severe occlusion, there remains a non-unique mapping
between the monocular image and hand mesh. To address this, we develop
DiffHand, the first diffusion-based framework that approaches hand mesh
reconstruction as a denoising diffusion process. Our one-stage pipeline
utilizes noise to model the uncertainty distribution of the intermediate hand
mesh in a forward process. We reformulate the denoising diffusion process to
gradually refine noisy hand mesh and then select mesh with the highest
probability of being correct based on the image itself, rather than relying on
2D joints extracted beforehand. To better model the connectivity of hand
vertices, we design a novel network module called the cross-modality decoder.
Extensive experiments on the popular benchmarks demonstrate that our method
outperforms the state-of-the-art hand mesh reconstruction approaches by
achieving 5.8mm PA-MPJPE on the Freihand test set, 4.98mm PA-MPJPE on the
DexYCB test set.
|
[
"cs.CV"
] | false |
2305.13752
|
2023-05-23T07:09:09Z
|
Pulling Target to Source: A New Perspective on Domain Adaptive Semantic
Segmentation
|
[
"Haochen Wang",
"Yujun Shen",
"Jingjing Fei",
"Wei Li",
"Liwei Wu",
"Yuxi Wang",
"Zhaoxiang Zhang"
] |
Domain adaptive semantic segmentation aims to transfer knowledge from a
labeled source domain to an unlabeled target domain. However, existing methods
primarily focus on directly learning qualified target features, making it
challenging to guarantee their discrimination in the absence of target labels.
This work provides a new perspective. We observe that the features learned with
source data manage to keep categorically discriminative during training,
thereby enabling us to implicitly learn adequate target representations by
simply \textbf{pulling target features close to source features for each
category}. To this end, we propose T2S-DA, which we interpret as a form of
pulling Target to Source for Domain Adaptation, encouraging the model in
learning similar cross-domain features. Also, considering the pixel categories
are heavily imbalanced for segmentation datasets, we come up with a dynamic
re-weighting strategy to help the model concentrate on those underperforming
classes. Extensive experiments confirm that T2S-DA learns a more discriminative
and generalizable representation, significantly surpassing the
state-of-the-art. We further show that our method is quite qualified for the
domain generalization task, verifying its domain-invariant property.
|
[
"cs.CV"
] | false |
2305.13800
|
2023-05-23T08:13:27Z
|
Generalizable Synthetic Image Detection via Language-guided Contrastive
Learning
|
[
"Haiwei Wu",
"Jiantao Zhou",
"Shile Zhang"
] |
The heightened realism of AI-generated images can be attributed to the rapid
development of synthetic models, including generative adversarial networks
(GANs) and diffusion models (DMs). The malevolent use of synthetic images, such
as the dissemination of fake news or the creation of fake profiles, however,
raises significant concerns regarding the authenticity of images. Though many
forensic algorithms have been developed for detecting synthetic images, their
performance, especially the generalization capability, is still far from being
adequate to cope with the increasing number of synthetic models. In this work,
we propose a simple yet very effective synthetic image detection method via a
language-guided contrastive learning and a new formulation of the detection
problem. We first augment the training images with carefully-designed textual
labels, enabling us to use a joint image-text contrastive learning for the
forensic feature extraction. In addition, we formulate the synthetic image
detection as an identification problem, which is vastly different from the
traditional classification-based approaches. It is shown that our proposed
LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved
generalizability to unseen image generation models and delivers promising
performance that far exceeds state-of-the-art competitors by +22.66% accuracy
and +15.24% AUC. The code is available at https://github.com/HighwayWu/LASTED.
|
[
"cs.CV"
] | false |
2305.13864
|
2023-05-23T09:36:27Z
|
MIANet: Aggregating Unbiased Instance and General Information for
Few-Shot Semantic Segmentation
|
[
"Yong Yang",
"Qiong Chen",
"Yuan Feng",
"Tianlin Huang"
] |
Existing few-shot segmentation methods are based on the meta-learning
strategy and extract instance knowledge from a support set and then apply the
knowledge to segment target objects in a query set. However, the extracted
knowledge is insufficient to cope with the variable intra-class differences
since the knowledge is obtained from a few samples in the support set. To
address the problem, we propose a multi-information aggregation network
(MIANet) that effectively leverages the general knowledge, i.e., semantic word
embeddings, and instance information for accurate segmentation. Specifically,
in MIANet, a general information module (GIM) is proposed to extract a general
class prototype from word embeddings as a supplement to instance information.
To this end, we design a triplet loss that treats the general class prototype
as an anchor and samples positive-negative pairs from local features in the
support set. The calculated triplet loss can transfer semantic similarities
among language identities from a word embedding space to a visual
representation space. To alleviate the model biasing towards the seen training
classes and to obtain multi-scale information, we then introduce a
non-parametric hierarchical prior module (HPM) to generate unbiased
instance-level information via calculating the pixel-level similarity between
the support and query image features. Finally, an information fusion module
(IFM) combines the general and instance information to make predictions for the
query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet
yields superior performance and set a new state-of-the-art. Code is available
at https://github.com/Aldrich2y/MIANet.
|
[
"cs.CV"
] | false |
2305.13909
|
2023-05-23T10:31:46Z
|
Temporal Contrastive Learning for Spiking Neural Networks
|
[
"Haonan Qiu",
"Zeyin Song",
"Yanqi Chen",
"Munan Ning",
"Wei Fang",
"Tao Sun",
"Zhengyu Ma",
"Li Yuan",
"Yonghong Tian"
] |
Biologically inspired spiking neural networks (SNNs) have garnered
considerable attention due to their low-energy consumption and spatio-temporal
information processing capabilities. Most existing SNNs training methods first
integrate output information across time steps, then adopt the cross-entropy
(CE) loss to supervise the prediction of the average representations. However,
in this work, we find the method above is not ideal for the SNNs training as it
omits the temporal dynamics of SNNs and degrades the performance quickly with
the decrease of inference time steps. One tempting method to model temporal
correlations is to apply the same label supervision at each time step and treat
them identically. Although it can acquire relatively consistent performance
across various time steps, it still faces challenges in obtaining SNNs with
high performance. Inspired by these observations, we propose Temporal-domain
supervised Contrastive Learning (TCL) framework, a novel method to obtain SNNs
with low latency and high performance by incorporating contrastive supervision
with temporal domain information. Contrastive learning (CL) prompts the network
to discern both consistency and variability in the representation space,
enabling it to better learn discriminative and generalizable features. We
extend this concept to the temporal domain of SNNs, allowing us to flexibly and
fully leverage the correlation between representations at different time steps.
Furthermore, we propose a Siamese Temporal-domain supervised Contrastive
Learning (STCL) framework to enhance the SNNs via augmentation, temporal and
class constraints simultaneously. Extensive experimental results demonstrate
that SNNs trained by our TCL and STCL can achieve both high performance and low
latency, achieving state-of-the-art performance on a variety of datasets (e.g.,
CIFAR-10, CIFAR-100, and DVS-CIFAR10).
|
[
"cs.CV"
] | false |
2305.13961
|
2023-05-23T11:40:12Z
|
Metrics Matter in Surgical Phase Recognition
|
[
"Isabel Funke",
"Dominik Rivoir",
"Stefanie Speidel"
] |
Surgical phase recognition is a basic component for different context-aware
applications in computer- and robot-assisted surgery. In recent years, several
methods for automatic surgical phase recognition have been proposed, showing
promising results. However, a meaningful comparison of these methods is
difficult due to differences in the evaluation process and incomplete reporting
of evaluation details. In particular, the details of metric computation can
vary widely between different studies. To raise awareness of potential
inconsistencies, this paper summarizes common deviations in the evaluation of
phase recognition algorithms on the Cholec80 benchmark. In addition, a
structured overview of previously reported evaluation results on Cholec80 is
provided, taking known differences in evaluation protocols into account.
Greater attention to evaluation details could help achieve more consistent and
comparable results on the surgical phase recognition task, leading to more
reliable conclusions about advancements in the field and, finally, translation
into clinical practice.
|
[
"cs.CV"
] | false |
2305.14039
|
2023-05-23T13:12:00Z
|
Learning a Single Convolutional Layer Model for Low Light Image
Enhancement
|
[
"Yuantong Zhang",
"Baoxin Teng",
"Daiqin Yang",
"Zhenzhong Chen",
"Haichuan Ma",
"Gang Li",
"Wenpeng Ding"
] |
Low-light image enhancement (LLIE) aims to improve the illuminance of images
due to insufficient light exposure. Recently, various lightweight
learning-based LLIE methods have been proposed to handle the challenges of
unfavorable prevailing low contrast, low brightness, etc. In this paper, we
have streamlined the architecture of the network to the utmost degree. By
utilizing the effective structural re-parameterization technique, a single
convolutional layer model (SCLM) is proposed that provides global low-light
enhancement as the coarsely enhanced results. In addition, we introduce a local
adaptation module that learns a set of shared parameters to accomplish local
illumination correction to address the issue of varied exposure levels in
different image regions. Experimental results demonstrate that the proposed
method performs favorably against the state-of-the-art LLIE methods in both
objective metrics and subjective visual effects. Additionally, our method has
fewer parameters and lower inference complexity compared to other
learning-based schemes.
|
[
"cs.CV"
] | false |
2305.14107
|
2023-05-23T14:27:41Z
|
Federated Generalized Category Discovery
|
[
"Nan Pu",
"Zhun Zhong",
"Xinyuan Ji",
"Nicu Sebe"
] |
Generalized category discovery (GCD) aims at grouping unlabeled samples from
known and unknown classes, given labeled data of known classes. To meet the
recent decentralization trend in the community, we introduce a practical yet
challenging task, namely Federated GCD (Fed-GCD), where the training data are
distributively stored in local clients and cannot be shared among clients. The
goal of Fed-GCD is to train a generic GCD model by client collaboration under
the privacy-protected constraint. The Fed-GCD leads to two challenges: 1)
representation degradation caused by training each client model with fewer data
than centralized GCD learning, and 2) highly heterogeneous label spaces across
different clients. To this end, we propose a novel Associated Gaussian
Contrastive Learning (AGCL) framework based on learnable GMMs, which consists
of a Client Semantics Association (CSA) and a global-local GMM Contrastive
Learning (GCL). On the server, CSA aggregates the heterogeneous categories of
local-client GMMs to generate a global GMM containing more comprehensive
category knowledge. On each client, GCL builds class-level contrastive learning
with both local and global GMMs. The local GCL learns robust representation
with limited local data. The global GCL encourages the model to produce more
discriminative representation with the comprehensive category relationships
that may not exist in local data. We build a benchmark based on six visual
datasets to facilitate the study of Fed-GCD. Extensive experiments show that
our AGCL outperforms the FedAvg-based baseline on all datasets.
|
[
"cs.CV"
] | false |
2305.14207
|
2023-05-23T16:26:56Z
|
SAD: Segment Any RGBD
|
[
"Jun Cen",
"Yizheng Wu",
"Kewei Wang",
"Xingyi Li",
"Jingkang Yang",
"Yixuan Pei",
"Lingdong Kong",
"Ziwei Liu",
"Qifeng Chen"
] |
The Segment Anything Model (SAM) has demonstrated its effectiveness in
segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis
on texture information while paying less attention to geometry information when
segmenting RGB images. To address this limitation, we propose the Segment Any
RGBD (SAD) model, which is specifically designed to extract geometry
information directly from images. Inspired by the natural ability of humans to
identify objects through the visualization of depth maps, SAD utilizes SAM to
segment the rendered depth map, thus providing cues with enhanced geometry
information and mitigating the issue of over-segmentation. We further include
the open-vocabulary semantic segmentation in our framework, so that the 3D
panoptic segmentation is fulfilled. The project is available on
https://github.com/Jun-CEN/SegmentAnyRGBD.
|
[
"cs.CV"
] | false |
2305.14298
|
2023-05-23T17:40:13Z
|
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
|
[
"En Yu",
"Tiancai Wang",
"Zhuoling Li",
"Yuang Zhang",
"Xiangyu Zhang",
"Wenbing Tao"
] |
Although end-to-end multi-object trackers like MOTR enjoy the merits of
simplicity, they suffer from the conflict between detection and association
seriously, resulting in unsatisfactory convergence dynamics. While MOTRv2
partly addresses this problem, it demands an additional detection network for
assistance. In this work, we serve as the first to reveal that this conflict
arises from the unfair label assignment between detect queries and track
queries during training, where these detect queries recognize targets and track
queries associate them. Based on this observation, we propose MOTRv3, which
balances the label assignment process using the developed release-fetch
supervision strategy. In this strategy, labels are first released for detection
and gradually fetched back for association. Besides, another two strategies
named pseudo label distillation and track group denoising are designed to
further improve the supervision for detection and association. Without the
assistance of an extra detection network during inference, MOTRv3 achieves
impressive performance across diverse benchmarks, e.g., MOT17, DanceTrack.
|
[
"cs.CV"
] | false |
2305.14306
|
2023-05-23T17:45:49Z
|
Hierarchical Adaptive Voxel-guided Sampling for Real-time Applications
in Large-scale Point Clouds
|
[
"Junyuan Ouyang",
"Xiao Liu",
"Haoyao Chen"
] |
While point-based neural architectures have demonstrated their efficacy, the
time-consuming sampler currently prevents them from performing real-time
reasoning on scene-level point clouds. Existing methods attempt to overcome
this issue by using random sampling strategy instead of the commonly-adopted
farthest point sampling~(FPS), but at the expense of lower performance. So the
effectiveness/efficiency trade-off remains under-explored. In this paper, we
reveal the key to high-quality sampling is ensuring an even spacing between
points in the subset, which can be naturally obtained through a grid. Based on
this insight, we propose a hierarchical adaptive voxel-guided point sampler
with linear complexity and high parallelization for real-time applications.
Extensive experiments on large-scale point cloud detection and segmentation
tasks demonstrate that our method achieves competitive performance with the
most powerful FPS, at an amazing speed that is more than 100 times faster. This
breakthrough in efficiency addresses the bottleneck of the sampling step when
handling scene-level point clouds. Furthermore, our sampler can be easily
integrated into existing models and achieves a 20$\sim$80\% reduction in
runtime with minimal effort. The code will be available at
https://github.com/OuyangJunyuan/pointcloud-3d-detector-tensorrt
|
[
"cs.CV"
] | false |
2305.14335
|
2023-05-23T17:58:05Z
|
Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud
Semantic Segmentation
|
[
"Shuting He",
"Xudong Jiang",
"Wei Jiang",
"Henghui Ding"
] |
In this work, we address the challenging task of few-shot and zero-shot 3D
point cloud semantic segmentation. The success of few-shot semantic
segmentation in 2D computer vision is mainly driven by the pre-training on
large-scale datasets like imagenet. The feature extractor pre-trained on
large-scale 2D datasets greatly helps the 2D few-shot learning. However, the
development of 3D deep learning is hindered by the limited volume and instance
modality of datasets due to the significant cost of 3D data collection and
annotation. This results in less representative features and large intra-class
feature variation for few-shot 3D point cloud segmentation. As a consequence,
directly extending existing popular prototypical methods of 2D few-shot
classification/segmentation into 3D point cloud segmentation won't work as well
as in 2D domain. To address this issue, we propose a Query-Guided Prototype
Adaption (QGPA) module to adapt the prototype from support point clouds feature
space to query point clouds feature space. With such prototype adaption, we
greatly alleviate the issue of large feature intra-class variation in point
cloud and significantly improve the performance of few-shot 3D segmentation.
Besides, to enhance the representation of prototypes, we introduce a
Self-Reconstruction (SR) module that enables prototype to reconstruct the
support mask as well as possible. Moreover, we further consider zero-shot 3D
point cloud semantic segmentation where there is no support sample. To this
end, we introduce category words as semantic information and propose a
semantic-visual projection model to bridge the semantic and visual spaces. Our
proposed method surpasses state-of-the-art algorithms by a considerable 7.90%
and 14.82% under the 2-way 1-shot setting on S3DIS and ScanNet benchmarks,
respectively. Code is available at https://github.com/heshuting555/PAP-FZS3D.
|
[
"cs.CV"
] | false |
2305.14462
|
2023-05-23T18:37:07Z
|
Sorted Convolutional Network for Achieving Continuous Rotational
Invariance
|
[
"Hanlin Mo",
"Guoying Zhao"
] |
The topic of achieving rotational invariance in convolutional neural networks
(CNNs) has gained considerable attention recently, as this invariance is
crucial for many computer vision tasks such as image classification and
matching. In this letter, we propose a Sorting Convolution (SC) inspired by
some hand-crafted features of texture images, which achieves continuous
rotational invariance without requiring additional learnable parameters or data
augmentation. Further, SC can directly replace the conventional convolution
operations in a classic CNN model to achieve its rotational invariance. Based
on MNIST-rot dataset, we first analyze the impact of convolutional kernel
sizes, different sampling and sorting strategies on SC's rotational invariance,
and compare our method with previous rotation-invariant CNN models. Then, we
combine SC with VGG, ResNet and DenseNet, and conduct classification
experiments on popular texture and remote sensing image datasets. Our results
demonstrate that SC achieves the best performance in the aforementioned tasks.
|
[
"cs.CV"
] | false |
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