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2305.20086
|
2023-05-31T17:58:02Z
|
Understanding and Mitigating Copying in Diffusion Models
|
[
"Gowthami Somepalli",
"Vasu Singla",
"Micah Goldblum",
"Jonas Geiping",
"Tom Goldstein"
] |
Images generated by diffusion models like Stable Diffusion are increasingly
widespread. Recent works and even lawsuits have shown that these models are
prone to replicating their training data, unbeknownst to the user. In this
paper, we first analyze this memorization problem in text-to-image diffusion
models. While it is widely believed that duplicated images in the training set
are responsible for content replication at inference time, we observe that the
text conditioning of the model plays a similarly important role. In fact, we
see in our experiments that data replication often does not happen for
unconditional models, while it is common in the text-conditional case.
Motivated by our findings, we then propose several techniques for reducing data
replication at both training and inference time by randomizing and augmenting
image captions in the training set.
|
[
"cs.LG",
"cs.CR",
"cs.CV"
] | true |
2306.00103
|
2023-05-31T18:23:57Z
|
ManagerTower: Aggregating the Insights of Uni-Modal Experts for
Vision-Language Representation Learning
|
[
"Xiao Xu",
"Bei Li",
"Chenfei Wu",
"Shao-Yen Tseng",
"Anahita Bhiwandiwalla",
"Shachar Rosenman",
"Vasudev Lal",
"Wanxiang Che",
"Nan Duan"
] |
Two-Tower Vision-Language (VL) models have shown promising improvements on
various downstream VL tasks. Although the most advanced work improves
performance by building bridges between encoders, it suffers from ineffective
layer-by-layer utilization of uni-modal representations and cannot flexibly
exploit different levels of uni-modal semantic knowledge. In this work, we
propose ManagerTower, a novel VL model architecture that gathers and combines
the insights of pre-trained uni-modal experts at different levels. The managers
introduced in each cross-modal layer can adaptively aggregate uni-modal
semantic knowledge to facilitate more comprehensive cross-modal alignment and
fusion. ManagerTower outperforms previous strong baselines both with and
without Vision-Language Pre-training (VLP). With only 4M VLP data, ManagerTower
achieves superior performances on various downstream VL tasks, especially
79.15% accuracy on VQAv2 Test-Std, 86.56% IR@1 and 95.64% TR@1 on Flickr30K.
Code and checkpoints are available at https://github.com/LooperXX/ManagerTower.
|
[
"cs.CV",
"cs.CL",
"cs.LG"
] | false |
2306.00180
|
2023-05-31T20:58:46Z
|
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses
via Pixel-Aligned Scene Flow
|
[
"Cameron Smith",
"Yilun Du",
"Ayush Tewari",
"Vincent Sitzmann"
] |
Reconstruction of 3D neural fields from posed images has emerged as a
promising method for self-supervised representation learning. The key challenge
preventing the deployment of these 3D scene learners on large-scale video data
is their dependence on precise camera poses from structure-from-motion, which
is prohibitively expensive to run at scale. We propose a method that jointly
reconstructs camera poses and 3D neural scene representations online and in a
single forward pass. We estimate poses by first lifting frame-to-frame optical
flow to 3D scene flow via differentiable rendering, preserving locality and
shift-equivariance of the image processing backbone. SE(3) camera pose
estimation is then performed via a weighted least-squares fit to the scene flow
field. This formulation enables us to jointly supervise pose estimation and a
generalizable neural scene representation via re-rendering the input video, and
thus, train end-to-end and fully self-supervised on real-world video datasets.
We demonstrate that our method performs robustly on diverse, real-world video,
notably on sequences traditionally challenging to optimization-based pose
estimation techniques.
|
[
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | false |
2306.00181
|
2023-05-31T20:59:47Z
|
Conditionally Strongly Log-Concave Generative Models
|
[
"Florentin Guth",
"Etienne Lempereur",
"Joan Bruna",
"Stéphane Mallat"
] |
There is a growing gap between the impressive results of deep image
generative models and classical algorithms that offer theoretical guarantees.
The former suffer from mode collapse or memorization issues, limiting their
application to scientific data. The latter require restrictive assumptions such
as log-concavity to escape the curse of dimensionality. We partially bridge
this gap by introducing conditionally strongly log-concave (CSLC) models, which
factorize the data distribution into a product of conditional probability
distributions that are strongly log-concave. This factorization is obtained
with orthogonal projectors adapted to the data distribution. It leads to
efficient parameter estimation and sampling algorithms, with theoretical
guarantees, although the data distribution is not globally log-concave. We show
that several challenging multiscale processes are conditionally log-concave
using wavelet packet orthogonal projectors. Numerical results are shown for
physical fields such as the $\varphi^4$ model and weak lensing convergence maps
with higher resolution than in previous works.
|
[
"stat.ML",
"cs.CV",
"cs.LG",
"eess.SP"
] | false |
2306.00188
|
2023-05-31T21:06:42Z
|
Multi-environment lifelong deep reinforcement learning for medical
imaging
|
[
"Guangyao Zheng",
"Shuhao Lai",
"Vladimir Braverman",
"Michael A. Jacobs",
"Vishwa S. Parekh"
] |
Deep reinforcement learning(DRL) is increasingly being explored in medical
imaging. However, the environments for medical imaging tasks are constantly
evolving in terms of imaging orientations, imaging sequences, and pathologies.
To that end, we developed a Lifelong DRL framework, SERIL to continually learn
new tasks in changing imaging environments without catastrophic forgetting.
SERIL was developed using selective experience replay based lifelong learning
technique for the localization of five anatomical landmarks in brain MRI on a
sequence of twenty-four different imaging environments. The performance of
SERIL, when compared to two baseline setups: MERT(multi-environment-best-case)
and SERT(single-environment-worst-case) demonstrated excellent performance with
an average distance of $9.90\pm7.35$ pixels from the desired landmark across
all 120 tasks, compared to $10.29\pm9.07$ for MERT and $36.37\pm22.41$ for
SERT($p<0.05$), demonstrating the excellent potential for continuously learning
multiple tasks across dynamically changing imaging environments.
|
[
"cs.LG",
"cs.CV",
"eess.IV"
] | false |
2306.00197
|
2023-05-31T21:28:08Z
|
SSL-CPCD: Self-supervised learning with composite pretext-class
discrimination for improved generalisability in endoscopic image analysis
|
[
"Ziang Xu",
"Jens Rittscher",
"Sharib Ali"
] |
Data-driven methods have shown tremendous progress in medical image analysis.
In this context, deep learning-based supervised methods are widely popular.
However, they require a large amount of training data and face issues in
generalisability to unseen datasets that hinder clinical translation.
Endoscopic imaging data incorporates large inter- and intra-patient variability
that makes these models more challenging to learn representative features for
downstream tasks. Thus, despite the publicly available datasets and datasets
that can be generated within hospitals, most supervised models still
underperform. While self-supervised learning has addressed this problem to some
extent in natural scene data, there is a considerable performance gap in the
medical image domain. In this paper, we propose to explore patch-level
instance-group discrimination and penalisation of inter-class variation using
additive angular margin within the cosine similarity metrics. Our novel
approach enables models to learn to cluster similar representative patches,
thereby improving their ability to provide better separation between different
classes. Our results demonstrate significant improvement on all metrics over
the state-of-the-art (SOTA) methods on the test set from the same and diverse
datasets. We evaluated our approach for classification, detection, and
segmentation. SSL-CPCD achieves 79.77% on Top 1 accuracy for ulcerative colitis
classification, 88.62% on mAP for polyp detection, and 82.32% on dice
similarity coefficient for segmentation tasks are nearly over 4%, 2%, and 3%,
respectively, compared to the baseline architectures. We also demonstrate that
our method generalises better than all SOTA methods to unseen datasets,
reporting nearly 7% improvement in our generalisability assessment.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2306.00228
|
2023-05-31T22:48:27Z
|
Using Visual Cropping to Enhance Fine-Detail Question Answering of
BLIP-Family Models
|
[
"Jiarui Zhang",
"Mahyar Khayatkhoei",
"Prateek Chhikara",
"Filip Ilievski"
] |
Visual Question Answering is a challenging task, as it requires seamless
interaction between perceptual, linguistic, and background knowledge systems.
While the recent progress of visual and natural language models like BLIP has
led to improved performance on this task, we lack understanding of the ability
of such models to perform on different kinds of questions and reasoning types.
As our initial analysis of BLIP-family models revealed difficulty with
answering fine-detail questions, we investigate the following question: Can
visual cropping be employed to improve the performance of state-of-the-art
visual question answering models on fine-detail questions? Given the recent
success of the BLIP-family models, we study a zero-shot and a fine-tuned BLIP
model. We define three controlled subsets of the popular VQA-v2 benchmark to
measure whether cropping can help model performance. Besides human cropping, we
devise two automatic cropping strategies based on multi-modal embedding by CLIP
and BLIP visual QA model gradients. Our experiments demonstrate that the
performance of BLIP model variants can be significantly improved through human
cropping, and automatic cropping methods can produce comparable benefits. A
deeper dive into our findings indicates that the performance enhancement is
more pronounced in zero-shot models than in fine-tuned models and more salient
with smaller bounding boxes than larger ones. We perform case studies to
connect quantitative differences with qualitative observations across question
types and datasets. Finally, we see that the cropping enhancement is robust, as
we gain an improvement of 4.59% (absolute) in the general VQA-random task by
simply inputting a concatenation of the original and gradient-based cropped
images. We make our code available to facilitate further innovation on visual
cropping methods for question answering.
|
[
"cs.CV",
"cs.AI",
"cs.CL"
] | false |
2306.06073
|
2023-05-31T20:27:10Z
|
Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient
Tree Cover Estimation
|
[
"Usman Nazir",
"Momin Uppal",
"Muhammad Tahir",
"Zubair Khalid"
] |
This paper proposes a multi-spectral random forest classifier with suitable
feature selection and masking for tree cover estimation in urban areas. The key
feature of the proposed classifier is filtering out the built-up region using
spectral indices followed by random forest classification on the remaining mask
with carefully selected features. Using Sentinel-2 satellite imagery, we
evaluate the performance of the proposed technique on a specified area
(approximately 82 acres) of Lahore University of Management Sciences (LUMS) and
demonstrate that our method outperforms a conventional random forest classifier
as well as state-of-the-art methods such as European Space Agency (ESA)
WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.
|
[
"cs.CV",
"cs.LG",
"eess.IV"
] | false |
2305.20074
|
2023-05-31T17:48:44Z
|
Feature Learning in Image Hierarchies using Functional Maximal
Correlation
|
[
"Bo Hu",
"Yuheng Bu",
"José C. Príncipe"
] |
This paper proposes the Hierarchical Functional Maximal Correlation Algorithm
(HFMCA), a hierarchical methodology that characterizes dependencies across two
hierarchical levels in multiview systems. By framing view similarities as
dependencies and ensuring contrastivity by imposing orthonormality, HFMCA
achieves faster convergence and increased stability in self-supervised
learning. HFMCA defines and measures dependencies within image hierarchies,
from pixels and patches to full images. We find that the network topology for
approximating orthonormal basis functions aligns with a vanilla CNN, enabling
the decomposition of density ratios between neighboring layers of feature maps.
This approach provides powerful interpretability, revealing the resemblance
between supervision and self-supervision through the lens of internal
representations.
|
[
"cs.CV",
"cs.AI",
"cs.IT",
"cs.LG",
"math.IT"
] | false |
2305.19474
|
2023-05-31T01:04:20Z
|
Ethical Considerations for Machine Translation of Indigenous Languages:
Giving a Voice to the Speakers
|
[
"Manuel Mager",
"Elisabeth Mager",
"Katharina Kann",
"Ngoc Thang Vu"
] |
In recent years machine translation has become very successful for
high-resource language pairs. This has also sparked new interest in research on
the automatic translation of low-resource languages, including Indigenous
languages. However, the latter are deeply related to the ethnic and cultural
groups that speak (or used to speak) them. The data collection, modeling and
deploying machine translation systems thus result in new ethical questions that
must be addressed. Motivated by this, we first survey the existing literature
on ethical considerations for the documentation, translation, and general
natural language processing for Indigenous languages. Afterward, we conduct and
analyze an interview study to shed light on the positions of community leaders,
teachers, and language activists regarding ethical concerns for the automatic
translation of their languages. Our results show that the inclusion, at
different degrees, of native speakers and community members is vital to
performing better and more ethical research on Indigenous languages.
|
[
"cs.CL"
] | false |
2305.19497
|
2023-05-31T02:15:15Z
|
Towards Flow Graph Prediction of Open-Domain Procedural Texts
|
[
"Keisuke Shirai",
"Hirotaka Kameko",
"Shinsuke Mori"
] |
Machine comprehension of procedural texts is essential for reasoning about
the steps and automating the procedures. However, this requires identifying
entities within a text and resolving the relationships between the entities.
Previous work focused on the cooking domain and proposed a framework to convert
a recipe text into a flow graph (FG) representation. In this work, we propose a
framework based on the recipe FG for flow graph prediction of open-domain
procedural texts. To investigate flow graph prediction performance in
non-cooking domains, we introduce the wikiHow-FG corpus from articles on
wikiHow, a website of how-to instruction articles. In experiments, we consider
using the existing recipe corpus and performing domain adaptation from the
cooking to the target domain. Experimental results show that the domain
adaptation models achieve higher performance than those trained only on the
cooking or target domain data.
|
[
"cs.CL"
] | false |
2305.19500
|
2023-05-31T02:17:04Z
|
Exploring Lottery Prompts for Pre-trained Language Models
|
[
"Yulin Chen",
"Ning Ding",
"Xiaobin Wang",
"Shengding Hu",
"Hai-Tao Zheng",
"Zhiyuan Liu",
"Pengjun Xie"
] |
Consistently scaling pre-trained language models (PLMs) imposes substantial
burdens on model adaptation, necessitating more efficient alternatives to
conventional fine-tuning. Given the advantage of prompting in the zero-shot
setting and the observed performance fluctuation among different prompts, we
explore the instance-level prompt and their generalizability. By searching
through the prompt space, we first validate the assumption that for every
instance, there is almost always a lottery prompt that induces the correct
prediction from the PLM, and such prompt can be obtained at a low cost thanks
to the inherent ability of PLMs. Meanwhile, we find that some strong lottery
prompts have high performance over the whole training set, and they are
equipped with distinguishable linguistic features. Lastly, we attempt to
generalize the searched strong lottery prompts to unseen data with prompt
ensembling method without any parameter tuning. Experiments are conducted on
various types of NLP classification tasks and demonstrate that the proposed
method can achieve comparable results with other gradient-free and
optimization-free baselines.
|
[
"cs.CL"
] | false |
2305.19549
|
2023-05-31T04:31:16Z
|
Accurate and Structured Pruning for Efficient Automatic Speech
Recognition
|
[
"Huiqiang Jiang",
"Li Lyna Zhang",
"Yuang Li",
"Yu Wu",
"Shijie Cao",
"Ting Cao",
"Yuqing Yang",
"Jinyu Li",
"Mao Yang",
"Lili Qiu"
] |
Automatic Speech Recognition (ASR) has seen remarkable advancements with deep
neural networks, such as Transformer and Conformer. However, these models
typically have large model sizes and high inference costs, posing a challenge
to deploy on resource-limited devices. In this paper, we propose a novel
compression strategy that leverages structured pruning and knowledge
distillation to reduce the model size and inference cost of the Conformer model
while preserving high recognition performance. Our approach utilizes a set of
binary masks to indicate whether to retain or prune each Conformer module, and
employs L0 regularization to learn the optimal mask values. To further enhance
pruning performance, we use a layerwise distillation strategy to transfer
knowledge from unpruned to pruned models. Our method outperforms all pruning
baselines on the widely used LibriSpeech benchmark, achieving a 50% reduction
in model size and a 28% reduction in inference cost with minimal performance
loss.
|
[
"cs.CL"
] | false |
2305.19589
|
2023-05-31T06:22:07Z
|
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with
BERT
|
[
"Aditya Yadavalli",
"Alekhya Yadavalli",
"Vera Tobin"
] |
Second language acquisition (SLA) research has extensively studied
cross-linguistic transfer, the influence of linguistic structure of a speaker's
native language [L1] on the successful acquisition of a foreign language [L2].
Effects of such transfer can be positive (facilitating acquisition) or negative
(impeding acquisition). We find that NLP literature has not given enough
attention to the phenomenon of negative transfer. To understand patterns of
both positive and negative transfer between L1 and L2, we model sequential
second language acquisition in LMs. Further, we build a Mutlilingual Age
Ordered CHILDES (MAO-CHILDES) -- a dataset consisting of 5 typologically
diverse languages, i.e., German, French, Polish, Indonesian, and Japanese -- to
understand the degree to which native Child-Directed Speech (CDS) [L1] can help
or conflict with English language acquisition [L2]. To examine the impact of
native CDS, we use the TILT-based cross lingual transfer learning approach
established by Papadimitriou and Jurafsky (2020) and find that, as in human
SLA, language family distance predicts more negative transfer. Additionally, we
find that conversational speech data shows greater facilitation for language
acquisition than scripted speech data. Our findings call for further research
using our novel Transformer-based SLA models and we would like to encourage it
by releasing our code, data, and models.
|
[
"cs.CL"
] | false |
2305.19650
|
2023-05-31T08:30:08Z
|
Adverbs, Surprisingly
|
[
"Dmitry Nikolaev",
"Collin F. Baker",
"Miriam R. L. Petruck",
"Sebastian Padó"
] |
This paper begins with the premise that adverbs are neglected in
computational linguistics. This view derives from two analyses: a literature
review and a novel adverb dataset to probe a state-of-the-art language model,
thereby uncovering systematic gaps in accounts for adverb meaning. We suggest
that using Frame Semantics for characterizing word meaning, as in FrameNet,
provides a promising approach to adverb analysis, given its ability to describe
ambiguity, semantic roles, and null instantiation.
|
[
"cs.CL"
] | false |
2305.19689
|
2023-05-31T09:34:26Z
|
Assessing Word Importance Using Models Trained for Semantic Tasks
|
[
"Dávid Javorský",
"Ondřej Bojar",
"François Yvon"
] |
Many NLP tasks require to automatically identify the most significant words
in a text. In this work, we derive word significance from models trained to
solve semantic task: Natural Language Inference and Paraphrase Identification.
Using an attribution method aimed to explain the predictions of these models,
we derive importance scores for each input token. We evaluate their relevance
using a so-called cross-task evaluation: Analyzing the performance of one model
on an input masked according to the other model's weight, we show that our
method is robust with respect to the choice of the initial task. Additionally,
we investigate the scores from the syntax point of view and observe interesting
patterns, e.g. words closer to the root of a syntactic tree receive higher
importance scores. Altogether, these observations suggest that our method can
be used to identify important words in sentences without any explicit word
importance labeling in training.
|
[
"cs.CL"
] | false |
2305.19707
|
2023-05-31T10:03:18Z
|
Building Extractive Question Answering System to Support Human-AI Health
Coaching Model for Sleep Domain
|
[
"Iva Bojic",
"Qi Chwen Ong",
"Shafiq Joty",
"Josip Car"
] |
Non-communicable diseases (NCDs) are a leading cause of global deaths,
necessitating a focus on primary prevention and lifestyle behavior change.
Health coaching, coupled with Question Answering (QA) systems, has the
potential to transform preventive healthcare. This paper presents a
human-Artificial Intelligence (AI) health coaching model incorporating a
domain-specific extractive QA system. A sleep-focused dataset, SleepQA, was
manually assembled and used to fine-tune domain-specific BERT models. The QA
system was evaluated using automatic and human methods. A data-centric
framework enhanced the system's performance by improving passage retrieval and
question reformulation. Although the system did not outperform the baseline in
automatic evaluation, it excelled in the human evaluation of real-world
questions. Integration into a Human-AI health coaching model was tested in a
pilot Randomized Controlled Trial (RCT).
|
[
"cs.CL"
] | false |
2305.19747
|
2023-05-31T11:20:48Z
|
Analyzing Text Representations by Measuring Task Alignment
|
[
"Cesar Gonzalez-Gutierrez",
"Audi Primadhanty",
"Francesco Cazzaro",
"Ariadna Quattoni"
] |
Textual representations based on pre-trained language models are key,
especially in few-shot learning scenarios. What makes a representation good for
text classification? Is it due to the geometric properties of the space or
because it is well aligned with the task? We hypothesize the second claim. To
test it, we develop a task alignment score based on hierarchical clustering
that measures alignment at different levels of granularity. Our experiments on
text classification validate our hypothesis by showing that task alignment can
explain the classification performance of a given representation.
|
[
"cs.CL"
] | false |
2305.19754
|
2023-05-31T11:39:10Z
|
Sentence Simplification Using Paraphrase Corpus for Initialization
|
[
"Kang Liu",
"Jipeng Qiang"
] |
Neural sentence simplification method based on sequence-to-sequence framework
has become the mainstream method for sentence simplification (SS) task.
Unfortunately, these methods are currently limited by the scarcity of parallel
SS corpus. In this paper, we focus on how to reduce the dependence on parallel
corpus by leveraging a careful initialization for neural SS methods from
paraphrase corpus. Our work is motivated by the following two findings: (1)
Paraphrase corpus includes a large proportion of sentence pairs belonging to SS
corpus. (2) We can construct large-scale pseudo parallel SS data by keeping
these sentence pairs with a higher complexity difference. Therefore, we propose
two strategies to initialize neural SS methods using paraphrase corpus. We
train three different neural SS methods with our initialization, which can
obtain substantial improvements on the available WikiLarge data compared with
themselves without initialization.
|
[
"cs.CL"
] | false |
2305.19757
|
2023-05-31T11:41:24Z
|
Automatic Discrimination of Human and Neural Machine Translation in
Multilingual Scenarios
|
[
"Malina Chichirau",
"Rik van Noord",
"Antonio Toral"
] |
We tackle the task of automatically discriminating between human and machine
translations. As opposed to most previous work, we perform experiments in a
multilingual setting, considering multiple languages and multilingual
pretrained language models. We show that a classifier trained on parallel data
with a single source language (in our case German-English) can still perform
well on English translations that come from different source languages, even
when the machine translations were produced by other systems than the one it
was trained on. Additionally, we demonstrate that incorporating the source text
in the input of a multilingual classifier improves (i) its accuracy and (ii)
its robustness on cross-system evaluation, compared to a monolingual
classifier. Furthermore, we find that using training data from multiple source
languages (German, Russian, and Chinese) tends to improve the accuracy of both
monolingual and multilingual classifiers. Finally, we show that bilingual
classifiers and classifiers trained on multiple source languages benefit from
being trained on longer text sequences, rather than on sentences.
|
[
"cs.CL"
] | false |
2305.19783
|
2023-05-31T12:19:40Z
|
IDAS: Intent Discovery with Abstractive Summarization
|
[
"Maarten De Raedt",
"Fréderic Godin",
"Thomas Demeester",
"Chris Develder"
] |
Intent discovery is the task of inferring latent intents from a set of
unlabeled utterances, and is a useful step towards the efficient creation of
new conversational agents. We show that recent competitive methods in intent
discovery can be outperformed by clustering utterances based on abstractive
summaries, i.e., "labels", that retain the core elements while removing
non-essential information. We contribute the IDAS approach, which collects a
set of descriptive utterance labels by prompting a Large Language Model,
starting from a well-chosen seed set of prototypical utterances, to bootstrap
an In-Context Learning procedure to generate labels for non-prototypical
utterances. The utterances and their resulting noisy labels are then encoded by
a frozen pre-trained encoder, and subsequently clustered to recover the latent
intents. For the unsupervised task (without any intent labels) IDAS outperforms
the state-of-the-art by up to +7.42% in standard cluster metrics for the
Banking, StackOverflow, and Transport datasets. For the semi-supervised task
(with labels for a subset of intents) IDAS surpasses 2 recent methods on the
CLINC benchmark without even using labeled data.
|
[
"cs.CL"
] | false |
2305.19845
|
2023-05-31T13:33:29Z
|
Guiding Computational Stance Detection with Expanded Stance Triangle
Framework
|
[
"Zhengyuan Liu",
"Yong Keong Yap",
"Hai Leong Chieu",
"Nancy F. Chen"
] |
Stance detection determines whether the author of a piece of text is in favor
of, against, or neutral towards a specified target, and can be used to gain
valuable insights into social media. The ubiquitous indirect referral of
targets makes this task challenging, as it requires computational solutions to
model semantic features and infer the corresponding implications from a literal
statement. Moreover, the limited amount of available training data leads to
subpar performance in out-of-domain and cross-target scenarios, as data-driven
approaches are prone to rely on superficial and domain-specific features. In
this work, we decompose the stance detection task from a linguistic
perspective, and investigate key components and inference paths in this task.
The stance triangle is a generic linguistic framework previously proposed to
describe the fundamental ways people express their stance. We further expand it
by characterizing the relationship between explicit and implicit objects. We
then use the framework to extend one single training corpus with additional
annotation. Experimental results show that strategically-enriched data can
significantly improve the performance on out-of-domain and cross-target
evaluation.
|
[
"cs.CL"
] | false |
2305.19857
|
2023-05-31T13:48:45Z
|
TPDM: Selectively Removing Positional Information for Zero-shot
Translation via Token-Level Position Disentangle Module
|
[
"Xingran Chen",
"Ge Zhang",
"Jie Fu"
] |
Due to Multilingual Neural Machine Translation's (MNMT) capability of
zero-shot translation, many works have been carried out to fully exploit the
potential of MNMT in zero-shot translation. It is often hypothesized that
positional information may hinder the MNMT from outputting a robust encoded
representation for decoding. However, previous approaches treat all the
positional information equally and thus are unable to selectively remove
certain positional information. In sharp contrast, this paper investigates how
to learn to selectively preserve useful positional information.
We describe the specific mechanism of positional information influencing MNMT
from the perspective of linguistics at the token level. We design a token-level
position disentangle module (TPDM) framework to disentangle positional
information at the token level based on the explanation. Our experiments
demonstrate that our framework improves zero-shot translation by a large margin
while reducing the performance loss in the supervised direction compared to
previous works.
|
[
"cs.CL"
] | false |
2305.19902
|
2023-05-31T14:35:53Z
|
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented
Generative Approach
|
[
"Jia Guo",
"Liying Cheng",
"Wenxuan Zhang",
"Stanley Kok",
"Xin Li",
"Lidong Bing"
] |
Argument mining involves multiple sub-tasks that automatically identify
argumentative elements, such as claim detection, evidence extraction, stance
classification, etc. However, each subtask alone is insufficient for a thorough
understanding of the argumentative structure and reasoning process. To learn a
complete view of an argument essay and capture the interdependence among
argumentative components, we need to know what opinions people hold (i.e.,
claims), why those opinions are valid (i.e., supporting evidence), which source
the evidence comes from (i.e., evidence type), and how those claims react to
the debating topic (i.e., stance). In this work, we for the first time propose
a challenging argument quadruplet extraction task (AQE), which can provide an
all-in-one extraction of four argumentative components, i.e., claims, evidence,
evidence types, and stances. To support this task, we construct a large-scale
and challenging dataset. However, there is no existing method that can solve
the argument quadruplet extraction. To fill this gap, we propose a novel
quad-tagging augmented generative approach, which leverages a quadruplet
tagging module to augment the training of the generative framework. The
experimental results on our dataset demonstrate the empirical superiority of
our proposed approach over several strong baselines.
|
[
"cs.CL"
] | false |
2305.19905
|
2023-05-31T14:38:14Z
|
How to Plant Trees in Language Models: Data and Architectural Effects on
the Emergence of Syntactic Inductive Biases
|
[
"Aaron Mueller",
"Tal Linzen"
] |
Accurate syntactic representations are essential for robust generalization in
natural language. Recent work has found that pre-training can teach language
models to rely on hierarchical syntactic features - as opposed to incorrect
linear features - when performing tasks after fine-tuning. We test what aspects
of pre-training are important for endowing encoder-decoder Transformers with an
inductive bias that favors hierarchical syntactic generalizations. We focus on
architectural features (depth, width, and number of parameters), as well as the
genre and size of the pre-training corpus, diagnosing inductive biases using
two syntactic transformation tasks: question formation and passivization, both
in English. We find that the number of parameters alone does not explain
hierarchical generalization: model depth plays greater role than model width.
We also find that pre-training on simpler language, such as child-directed
speech, induces a hierarchical bias using an order-of-magnitude less data than
pre-training on more typical datasets based on web text or Wikipedia; this
suggests that in cognitively plausible language acquisition settings, neural
language models may be more data-efficient than previously thought.
|
[
"cs.CL"
] | false |
2305.19974
|
2023-05-31T16:01:57Z
|
Correcting Semantic Parses with Natural Language through Dynamic Schema
Encoding
|
[
"Parker Glenn",
"Parag Pravin Dakle",
"Preethi Raghavan"
] |
In addressing the task of converting natural language to SQL queries, there
are several semantic and syntactic challenges. It becomes increasingly
important to understand and remedy the points of failure as the performance of
semantic parsing systems improve. We explore semantic parse correction with
natural language feedback, proposing a new solution built on the success of
autoregressive decoders in text-to-SQL tasks. By separating the semantic and
syntactic difficulties of the task, we show that the accuracy of text-to-SQL
parsers can be boosted by up to 26% with only one turn of correction with
natural language. Additionally, we show that a T5-base model is capable of
correcting the errors of a T5-large model in a zero-shot, cross-parser setting.
|
[
"cs.CL"
] | false |
2305.20080
|
2023-05-31T17:55:21Z
|
Findings of the VarDial Evaluation Campaign 2023
|
[
"Noëmi Aepli",
"Çağrı Çöltekin",
"Rob Van Der Goot",
"Tommi Jauhiainen",
"Mourhaf Kazzaz",
"Nikola Ljubešić",
"Kai North",
"Barbara Plank",
"Yves Scherrer",
"Marcos Zampieri"
] |
This report presents the results of the shared tasks organized as part of the
VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on
Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects
(VarDial), co-located with EACL 2023. Three separate shared tasks were included
this year: Slot and intent detection for low-resource language varieties
(SID4LR), Discriminating Between Similar Languages -- True Labels (DSL-TL), and
Discriminating Between Similar Languages -- Speech (DSL-S). All three tasks
were organized for the first time this year.
|
[
"cs.CL"
] | false |
2306.00100
|
2023-05-31T18:22:33Z
|
MetaXLR -- Mixed Language Meta Representation Transformation for
Low-resource Cross-lingual Learning based on Multi-Armed Bandit
|
[
"Liat Bezalel",
"Eyal Orgad"
] |
Transfer learning for extremely low resource languages is a challenging task
as there is no large scale monolingual corpora for pre training or sufficient
annotated data for fine tuning. We follow the work of MetaXL which suggests
using meta learning for transfer learning from a single source language to an
extremely low resource one. We propose an enhanced approach which uses multiple
source languages chosen in a data driven manner. In addition, we introduce a
sample selection strategy for utilizing the languages in training by using a
multi armed bandit algorithm. Using both of these improvements we managed to
achieve state of the art results on the NER task for the extremely low resource
languages while using the same amount of data, making the representations
better generalized. Also, due to the method ability to use multiple languages
it allows the framework to use much larger amounts of data, while still having
superior results over the former MetaXL method even with the same amounts of
data.
|
[
"cs.CL"
] | false |
2306.00121
|
2023-05-31T18:52:41Z
|
Multilingual Multi-Figurative Language Detection
|
[
"Huiyuan Lai",
"Antonio Toral",
"Malvina Nissim"
] |
Figures of speech help people express abstract concepts and evoke stronger
emotions than literal expressions, thereby making texts more creative and
engaging. Due to its pervasive and fundamental character, figurative language
understanding has been addressed in Natural Language Processing, but it's
highly understudied in a multilingual setting and when considering more than
one figure of speech at the same time. To bridge this gap, we introduce
multilingual multi-figurative language modelling, and provide a benchmark for
sentence-level figurative language detection, covering three common figures of
speech and seven languages. Specifically, we develop a framework for figurative
language detection based on template-based prompt learning. In so doing, we
unify multiple detection tasks that are interrelated across multiple figures of
speech and languages, without requiring task- or language-specific modules.
Experimental results show that our framework outperforms several strong
baselines and may serve as a blueprint for the joint modelling of other
interrelated tasks.
|
[
"cs.CL"
] | false |
2306.00124
|
2023-05-31T19:00:33Z
|
Pre-Trained Language-Meaning Models for Multilingual Parsing and
Generation
|
[
"Chunliu Wang",
"Huiyuan Lai",
"Malvina Nissim",
"Johan Bos"
] |
Pre-trained language models (PLMs) have achieved great success in NLP and
have recently been used for tasks in computational semantics. However, these
tasks do not fully benefit from PLMs since meaning representations are not
explicitly included in the pre-training stage. We introduce multilingual
pre-trained language-meaning models based on Discourse Representation
Structures (DRSs), including meaning representations besides natural language
texts in the same model, and design a new strategy to reduce the gap between
the pre-training and fine-tuning objectives. Since DRSs are language neutral,
cross-lingual transfer learning is adopted to further improve the performance
of non-English tasks. Automatic evaluation results show that our approach
achieves the best performance on both the multilingual DRS parsing and
DRS-to-text generation tasks. Correlation analysis between automatic metrics
and human judgements on the generation task further validates the effectiveness
of our model. Human inspection reveals that out-of-vocabulary tokens are the
main cause of erroneous results.
|
[
"cs.CL"
] | false |
2306.00137
|
2023-05-31T19:28:00Z
|
A Sequence-to-Sequence&Set Model for Text-to-Table Generation
|
[
"Tong Li",
"Zhihao Wang",
"Liangying Shao",
"Xuling Zheng",
"Xiaoli Wang",
"Jinsong Su"
] |
Recently, the text-to-table generation task has attracted increasing
attention due to its wide applications. In this aspect, the dominant model
formalizes this task as a sequence-to-sequence generation task and serializes
each table into a token sequence during training by concatenating all rows in a
top-down order. However, it suffers from two serious defects: 1) the predefined
order introduces a wrong bias during training, which highly penalizes shifts in
the order between rows; 2) the error propagation problem becomes serious when
the model outputs a long token sequence. In this paper, we first conduct a
preliminary study to demonstrate the generation of most rows is
order-insensitive. Furthermore, we propose a novel sequence-to-sequence&set
text-to-table generation model. Specifically, in addition to a text encoder
encoding the input text, our model is equipped with a table header generator to
first output a table header, i.e., the first row of the table, in the manner of
sequence generation. Then we use a table body generator with learnable row
embeddings and column embeddings to generate a set of table body rows in
parallel. Particularly, to deal with the issue that there is no correspondence
between each generated table body row and target during training, we propose a
target assignment strategy based on the bipartite matching between the first
cells of generated table body rows and targets. Experiment results show that
our model significantly surpasses the baselines, achieving state-of-the-art
performance on commonly-used datasets.
|
[
"cs.CL"
] | false |
2306.00177
|
2023-05-31T20:54:43Z
|
Contrastive Hierarchical Discourse Graph for Scientific Document
Summarization
|
[
"Haopeng Zhang",
"Xiao Liu",
"Jiawei Zhang"
] |
The extended structural context has made scientific paper summarization a
challenging task. This paper proposes CHANGES, a contrastive hierarchical graph
neural network for extractive scientific paper summarization. CHANGES
represents a scientific paper with a hierarchical discourse graph and learns
effective sentence representations with dedicated designed hierarchical graph
information aggregation. We also propose a graph contrastive learning module to
learn global theme-aware sentence representations. Extensive experiments on the
PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the
importance of capturing hierarchical structure information in modeling
scientific papers.
|
[
"cs.CL"
] | false |
2306.00186
|
2023-05-31T21:04:04Z
|
Factually Consistent Summarization via Reinforcement Learning with
Textual Entailment Feedback
|
[
"Paul Roit",
"Johan Ferret",
"Lior Shani",
"Roee Aharoni",
"Geoffrey Cideron",
"Robert Dadashi",
"Matthieu Geist",
"Sertan Girgin",
"Léonard Hussenot",
"Orgad Keller",
"Nikola Momchev",
"Sabela Ramos",
"Piotr Stanczyk",
"Nino Vieillard",
"Olivier Bachem",
"Gal Elidan",
"Avinatan Hassidim",
"Olivier Pietquin",
"Idan Szpektor"
] |
Despite the seeming success of contemporary grounded text generation systems,
they often tend to generate factually inconsistent text with respect to their
input. This phenomenon is emphasized in tasks like summarization, in which the
generated summaries should be corroborated by their source article. In this
work, we leverage recent progress on textual entailment models to directly
address this problem for abstractive summarization systems. We use
reinforcement learning with reference-free, textual entailment rewards to
optimize for factual consistency and explore the ensuing trade-offs, as
improved consistency may come at the cost of less informative or more
extractive summaries. Our results, according to both automatic metrics and
human evaluation, show that our method considerably improves the faithfulness,
salience, and conciseness of the generated summaries.
|
[
"cs.CL"
] | false |
2305.19584
|
2023-05-31T06:09:11Z
|
The Tag-Team Approach: Leveraging CLS and Language Tagging for Enhancing
Multilingual ASR
|
[
"Kaousheik Jayakumar",
"Vrunda N. Sukhadia",
"A Arunkumar",
"S. Umesh"
] |
Building a multilingual Automated Speech Recognition (ASR) system in a
linguistically diverse country like India can be a challenging task due to the
differences in scripts and the limited availability of speech data. This
problem can be solved by exploiting the fact that many of these languages are
phonetically similar. These languages can be converted into a Common Label Set
(CLS) by mapping similar sounds to common labels. In this paper, new approaches
are explored and compared to improve the performance of CLS based multilingual
ASR model. Specific language information is infused in the ASR model by giving
Language ID or using CLS to Native script converter on top of the CLS
Multilingual model. These methods give a significant improvement in Word Error
Rate (WER) compared to the CLS baseline. These methods are further tried on
out-of-distribution data to check their robustness.
|
[
"cs.CL",
"eess.AS"
] | false |
2305.19585
|
2023-05-31T06:09:59Z
|
LAIT: Efficient Multi-Segment Encoding in Transformers with
Layer-Adjustable Interaction
|
[
"Jeremiah Milbauer",
"Annie Louis",
"Mohammad Javad Hosseini",
"Alex Fabrikant",
"Donald Metzler",
"Tal Schuster"
] |
Transformer encoders contextualize token representations by attending to all
other tokens at each layer, leading to quadratic increase in compute effort
with the input length. In practice, however, the input text of many NLP tasks
can be seen as a sequence of related segments (e.g., the sequence of sentences
within a passage, or the hypothesis and premise in NLI). While attending across
these segments is highly beneficial for many tasks, we hypothesize that this
interaction can be delayed until later encoding stages.
To this end, we introduce Layer-Adjustable Interactions in Transformers
(LAIT). Within LAIT, segmented inputs are first encoded independently, and then
jointly. This partial two-tower architecture bridges the gap between a Dual
Encoder's ability to pre-compute representations for segments and a fully
self-attentive Transformer's capacity to model cross-segment attention. The
LAIT framework effectively leverages existing pretrained Transformers and
converts them into the hybrid of the two aforementioned architectures, allowing
for easy and intuitive control over the performance-efficiency tradeoff.
Experimenting on a wide range of NLP tasks, we find LAIT able to reduce 30-50%
of the attention FLOPs on many tasks, while preserving high accuracy; in some
practical settings, LAIT could reduce actual latency by orders of magnitude.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.19597
|
2023-05-31T06:45:09Z
|
What does the Failure to Reason with "Respectively" in Zero/Few-Shot
Settings Tell Us about Language Models?
|
[
"Ruixiang Cui",
"Seolhwa Lee",
"Daniel Hershcovich",
"Anders Søgaard"
] |
Humans can effortlessly understand the coordinate structure of sentences such
as "Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle,
respectively". In the context of natural language inference (NLI), we examine
how language models (LMs) reason with respective readings (Gawron and Kehler,
2004) from two perspectives: syntactic-semantic and commonsense-world
knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally
occurring dataset NatResNLI to encompass various explicit and implicit
realizations of "respectively". We show that fine-tuned NLI models struggle
with understanding such readings without explicit supervision. While few-shot
learning is easy in the presence of explicit cues, longer training is required
when the reading is evoked implicitly, leaving models to rely on common sense
inferences. Furthermore, our fine-grained analysis indicates models fail to
generalize across different constructions. To conclude, we demonstrate that LMs
still lag behind humans in generalizing to the long tail of linguistic
constructions.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.19607
|
2023-05-31T07:23:46Z
|
Adversarial Clean Label Backdoor Attacks and Defenses on Text
Classification Systems
|
[
"Ashim Gupta",
"Amrith Krishna"
] |
Clean-label (CL) attack is a form of data poisoning attack where an adversary
modifies only the textual input of the training data, without requiring access
to the labeling function. CL attacks are relatively unexplored in NLP, as
compared to label flipping (LF) attacks, where the latter additionally requires
access to the labeling function as well. While CL attacks are more resilient to
data sanitization and manual relabeling methods than LF attacks, they often
demand as high as ten times the poisoning budget than LF attacks. In this work,
we first introduce an Adversarial Clean Label attack which can adversarially
perturb in-class training examples for poisoning the training set. We then show
that an adversary can significantly bring down the data requirements for a CL
attack, using the aforementioned approach, to as low as 20% of the data
otherwise required. We then systematically benchmark and analyze a number of
defense methods, for both LF and CL attacks, some previously employed solely
for LF attacks in the textual domain and others adapted from computer vision.
We find that text-specific defenses greatly vary in their effectiveness
depending on their properties.
|
[
"cs.CL",
"cs.CR"
] | false |
2305.19759
|
2023-05-31T11:43:16Z
|
Simple yet Effective Code-Switching Language Identification with
Multitask Pre-Training and Transfer Learning
|
[
"Shuyue Stella Li",
"Cihan Xiao",
"Tianjian Li",
"Bismarck Odoom"
] |
Code-switching, also called code-mixing, is the linguistics phenomenon where
in casual settings, multilingual speakers mix words from different languages in
one utterance. Due to its spontaneous nature, code-switching is extremely
low-resource, which makes it a challenging problem for language and speech
processing tasks. In such contexts, Code-Switching Language Identification
(CSLID) becomes a difficult but necessary task if we want to maximally leverage
existing monolingual tools for other tasks. In this work, we propose two novel
approaches toward improving language identification accuracy on an
English-Mandarin child-directed speech dataset. Our methods include a stacked
Residual CNN+GRU model and a multitask pre-training approach to use Automatic
Speech Recognition (ASR) as an auxiliary task for CSLID. Due to the
low-resource nature of code-switching, we also employ careful silver data
creation using monolingual corpora in both languages and up-sampling as data
augmentation. We focus on English-Mandarin code-switched data, but our method
works on any language pair. Our best model achieves a balanced accuracy of
0.781 on a real English-Mandarin code-switching child-directed speech corpus
and outperforms the previous baseline by 55.3%.
|
[
"cs.CL",
"eess.AS"
] | false |
2305.19835
|
2023-05-31T13:23:04Z
|
Deliberate then Generate: Enhanced Prompting Framework for Text
Generation
|
[
"Bei Li",
"Rui Wang",
"Junliang Guo",
"Kaitao Song",
"Xu Tan",
"Hany Hassan",
"Arul Menezes",
"Tong Xiao",
"Jiang Bian",
"JingBo Zhu"
] |
Large language models (LLMs) have shown remarkable success across a wide
range of natural language generation tasks, where proper prompt designs make
great impacts. While existing prompting methods are normally restricted to
providing correct information, in this paper, we encourage the model to
deliberate by proposing a novel Deliberate then Generate (DTG) prompting
framework, which consists of error detection instructions and candidates that
may contain errors. DTG is a simple yet effective technique that can be applied
to various text generation tasks with minimal modifications. We conduct
extensive experiments on 20+ datasets across 7 text generation tasks, including
summarization, translation, dialogue, and more. We show that DTG consistently
outperforms existing prompting methods and achieves state-of-the-art
performance on multiple text generation tasks. We also provide in-depth
analyses to reveal the underlying mechanisms of DTG, which may inspire future
research on prompting for LLMs.
|
[
"cs.CL",
"cs.AI"
] | true |
2305.19847
|
2023-05-31T13:36:51Z
|
How Does Pretraining Improve Discourse-Aware Translation?
|
[
"Zhihong Huang",
"Longyue Wang",
"Siyou Liu",
"Derek F. Wong"
] |
Pretrained language models (PLMs) have produced substantial improvements in
discourse-aware neural machine translation (NMT), for example, improved
coherence in spoken language translation. However, the underlying reasons for
their strong performance have not been well explained. To bridge this gap, we
introduce a probing task to interpret the ability of PLMs to capture discourse
relation knowledge. We validate three state-of-the-art PLMs across encoder-,
decoder-, and encoder-decoder-based models. The analysis shows that (1) the
ability of PLMs on discourse modelling varies from architecture and layer; (2)
discourse elements in a text lead to different learning difficulties for PLMs.
Besides, we investigate the effects of different PLMs on spoken language
translation. Through experiments on IWSLT2017 Chinese-English dataset, we
empirically reveal that NMT models initialized from different layers of PLMs
exhibit the same trends with the probing task. Our findings are instructive to
understand how and when discourse knowledge in PLMs should work for downstream
tasks.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.19911
|
2023-05-31T14:44:33Z
|
Neuron to Graph: Interpreting Language Model Neurons at Scale
|
[
"Alex Foote",
"Neel Nanda",
"Esben Kran",
"Ioannis Konstas",
"Shay Cohen",
"Fazl Barez"
] |
Advances in Large Language Models (LLMs) have led to remarkable capabilities,
yet their inner mechanisms remain largely unknown. To understand these models,
we need to unravel the functions of individual neurons and their contribution
to the network. This paper introduces a novel automated approach designed to
scale interpretability techniques across a vast array of neurons within LLMs,
to make them more interpretable and ultimately safe. Conventional methods
require examination of examples with strong neuron activation and manual
identification of patterns to decipher the concepts a neuron responds to. We
propose Neuron to Graph (N2G), an innovative tool that automatically extracts a
neuron's behaviour from the dataset it was trained on and translates it into an
interpretable graph. N2G uses truncation and saliency methods to emphasise only
the most pertinent tokens to a neuron while enriching dataset examples with
diverse samples to better encompass the full spectrum of neuron behaviour.
These graphs can be visualised to aid researchers' manual interpretation, and
can generate token activations on text for automatic validation by comparison
with the neuron's ground truth activations, which we use to show that the model
is better at predicting neuron activation than two baseline methods. We also
demonstrate how the generated graph representations can be flexibly used to
facilitate further automation of interpretability research, by searching for
neurons with particular properties, or programmatically comparing neurons to
each other to identify similar neurons. Our method easily scales to build graph
representations for all neurons in a 6-layer Transformer model using a single
Tesla T4 GPU, allowing for wide usability. We release the code and instructions
for use at https://github.com/alexjfoote/Neuron2Graph.
|
[
"cs.LG",
"cs.CL"
] | false |
2305.19936
|
2023-05-31T15:20:54Z
|
Metropolis-Hastings algorithm in joint-attention naming game:
Experimental semiotics study
|
[
"Ryota Okumura",
"Tadahiro Taniguchi",
"Yosinobu Hagiwara",
"Akira Taniguchi"
] |
In this study, we explore the emergence of symbols during interactions
between individuals through an experimental semiotic study. Previous studies
investigate how humans organize symbol systems through communication using
artificially designed subjective experiments. In this study, we have focused on
a joint attention-naming game (JA-NG) in which participants independently
categorize objects and assign names while assuming their joint attention.
In the theory of the Metropolis-Hastings naming game (MHNG), listeners accept
provided names according to the acceptance probability computed using the
Metropolis-Hastings (MH) algorithm. The theory of MHNG suggests that symbols
emerge as an approximate decentralized Bayesian inference of signs, which is
represented as a shared prior variable if the conditions of MHNG are satisfied.
This study examines whether human participants exhibit behavior consistent
with MHNG theory when playing JA-NG. By comparing human acceptance decisions of
a partner's naming with acceptance probabilities computed in the MHNG, we
tested whether human behavior is consistent with the MHNG theory. The main
contributions of this study are twofold. First, we reject the null hypothesis
that humans make acceptance judgments with a constant probability, regardless
of the acceptance probability calculated by the MH algorithm. This result
suggests that people followed the acceptance probability computed by the MH
algorithm to some extent. Second, the MH-based model predicted human
acceptance/rejection behavior more accurately than the other four models:
Constant, Numerator, Subtraction, and Binary. This result indicates that symbol
emergence in JA-NG can be explained using MHNG and is considered an approximate
decentralized Bayesian inference.
|
[
"cs.CL",
"cs.HC"
] | false |
2305.19998
|
2023-05-31T16:19:13Z
|
Efficient Shapley Values Estimation by Amortization for Text
Classification
|
[
"Chenghao Yang",
"Fan Yin",
"He He",
"Kai-Wei Chang",
"Xiaofei Ma",
"Bing Xiang"
] |
Despite the popularity of Shapley Values in explaining neural text
classification models, computing them is prohibitive for large pretrained
models due to a large number of model evaluations. In practice, Shapley Values
are often estimated with a small number of stochastic model evaluations.
However, we show that the estimated Shapley Values are sensitive to random seed
choices -- the top-ranked features often have little overlap across different
seeds, especially on examples with longer input texts. This can only be
mitigated by aggregating thousands of model evaluations, which on the other
hand, induces substantial computational overheads. To mitigate the trade-off
between stability and efficiency, we develop an amortized model that directly
predicts each input feature's Shapley Value without additional model
evaluations. It is trained on a set of examples whose Shapley Values are
estimated from a large number of model evaluations to ensure stability.
Experimental results on two text classification datasets demonstrate that our
amortized model estimates Shapley Values accurately with up to 60 times speedup
compared to traditional methods. Furthermore, the estimated values are stable
as the inference is deterministic. We release our code at
https://github.com/yangalan123/Amortized-Interpretability.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.20018
|
2023-05-31T16:47:20Z
|
Scalable Learning of Latent Language Structure With Logical Offline
Cycle Consistency
|
[
"Maxwell Crouse",
"Ramon Astudillo",
"Tahira Naseem",
"Subhajit Chaudhury",
"Pavan Kapanipathi",
"Salim Roukos",
"Alexander Gray"
] |
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a
scalable, semi-supervised method for training a neural semantic parser.
Conceptually, LOCCO can be viewed as a form of self-learning where the semantic
parser being trained is used to generate annotations for unlabeled text that
are then used as new supervision. To increase the quality of annotations, our
method utilizes a count-based prior over valid formal meaning representations
and a cycle-consistency score produced by a neural text generation model as
additional signals. Both the prior and semantic parser are updated in an
alternate fashion from full passes over the training data, which can be seen as
approximating the marginalization of latent structures through stochastic
variational inference. The use of a count-based prior, frozen text generation
model, and offline annotation process yields an approach with negligible
complexity and latency increases as compared to conventional self-learning. As
an added bonus, the annotations produced by LOCCO can be trivially repurposed
to train a neural text generation model. We demonstrate the utility of LOCCO on
the well-known WebNLG benchmark where we obtain an improvement of 2 points
against a self-learning parser under equivalent conditions, an improvement of
1.3 points against the previous state-of-the-art parser, and competitive text
generation performance in terms of BLEU score.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.20045
|
2023-05-31T17:18:47Z
|
ActiveAED: A Human in the Loop Improves Annotation Error Detection
|
[
"Leon Weber",
"Barbara Plank"
] |
Manually annotated datasets are crucial for training and evaluating Natural
Language Processing models. However, recent work has discovered that even
widely-used benchmark datasets contain a substantial number of erroneous
annotations. This problem has been addressed with Annotation Error Detection
(AED) models, which can flag such errors for human re-annotation. However, even
though many of these AED methods assume a final curation step in which a human
annotator decides whether the annotation is erroneous, they have been developed
as static models without any human-in-the-loop component. In this work, we
propose ActiveAED, an AED method that can detect errors more accurately by
repeatedly querying a human for error corrections in its prediction loop. We
evaluate ActiveAED on eight datasets spanning five different tasks and find
that it leads to improvements over the state of the art on seven of them, with
gains of up to six percentage points in average precision.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.00176
|
2023-05-31T20:50:45Z
|
Automated Annotation with Generative AI Requires Validation
|
[
"Nicholas Pangakis",
"Samuel Wolken",
"Neil Fasching"
] |
Generative large language models (LLMs) can be a powerful tool for augmenting
text annotation procedures, but their performance varies across annotation
tasks due to prompt quality, text data idiosyncrasies, and conceptual
difficulty. Because these challenges will persist even as LLM technology
improves, we argue that any automated annotation process using an LLM must
validate the LLM's performance against labels generated by humans. To this end,
we outline a workflow to harness the annotation potential of LLMs in a
principled, efficient way. Using GPT-4, we validate this approach by
replicating 27 annotation tasks across 11 datasets from recent social science
articles in high-impact journals. We find that LLM performance for text
annotation is promising but highly contingent on both the dataset and the type
of annotation task, which reinforces the necessity to validate on a
task-by-task basis. We make available easy-to-use software designed to
implement our workflow and streamline the deployment of LLMs for automated
annotation.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.00198
|
2023-05-31T21:35:08Z
|
An Invariant Learning Characterization of Controlled Text Generation
|
[
"Carolina Zheng",
"Claudia Shi",
"Keyon Vafa",
"Amir Feder",
"David M. Blei"
] |
Controlled generation refers to the problem of creating text that contains
stylistic or semantic attributes of interest. Many approaches reduce this
problem to training a predictor of the desired attribute. For example,
researchers hoping to deploy a large language model to produce non-toxic
content may use a toxicity classifier to filter generated text. In practice,
the generated text to classify, which is determined by user prompts, may come
from a wide range of distributions. In this paper, we show that the performance
of controlled generation may be poor if the distributions of text in response
to user prompts differ from the distribution the predictor was trained on. To
address this problem, we cast controlled generation under distribution shift as
an invariant learning problem: the most effective predictor should be invariant
across multiple text environments. We then discuss a natural solution that
arises from this characterization and propose heuristics for selecting natural
environments. We study this characterization and the proposed method
empirically using both synthetic and real data. Experiments demonstrate both
the challenge of distribution shift in controlled generation and the potential
of invariance methods in this setting.
|
[
"cs.CL",
"cs.LG"
] | false |
2306.01004
|
2023-05-31T11:50:43Z
|
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based
Sentiment Analysis
|
[
"Ru Zhou",
"Wenya Guo",
"Xumeng Liu",
"Shenglong Yu",
"Ying Zhang",
"Xiaojie Yuan"
] |
Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects
from text-image pairs and recognize their sentiments. Existing methods make
great efforts to align the whole image to corresponding aspects. However,
different regions of the image may relate to different aspects in the same
sentence, and coarsely establishing image-aspect alignment will introduce noise
to aspect-based sentiment analysis (i.e., visual noise). Besides, the sentiment
of a specific aspect can also be interfered by descriptions of other aspects
(i.e., textual noise). Considering the aforementioned noises, this paper
proposes an Aspect-oriented Method (AoM) to detect aspect-relevant semantic and
sentiment information. Specifically, an aspect-aware attention module is
designed to simultaneously select textual tokens and image blocks that are
semantically related to the aspects. To accurately aggregate sentiment
information, we explicitly introduce sentiment embedding into AoM, and use a
graph convolutional network to model the vision-text and text-text interaction.
Extensive experiments demonstrate the superiority of AoM to existing methods.
The source code is publicly released at https://github.com/SilyRab/AoM.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.19563
|
2023-05-31T05:17:17Z
|
Zero-Shot Automatic Pronunciation Assessment
|
[
"Hongfu Liu",
"Mingqian Shi",
"Ye Wang"
] |
Automatic Pronunciation Assessment (APA) is vital for computer-assisted
language learning. Prior methods rely on annotated speech-text data to train
Automatic Speech Recognition (ASR) models or speech-score data to train
regression models. In this work, we propose a novel zero-shot APA method based
on the pre-trained acoustic model, HuBERT. Our method involves encoding speech
input and corrupting them via a masking module. We then employ the Transformer
encoder and apply k-means clustering to obtain token sequences. Finally, a
scoring module is designed to measure the number of wrongly recovered tokens.
Experimental results on speechocean762 demonstrate that the proposed method
achieves comparable performance to supervised regression baselines and
outperforms non-regression baselines in terms of Pearson Correlation
Coefficient (PCC). Additionally, we analyze how masking strategies affect the
performance of APA.
|
[
"cs.SD",
"cs.CL",
"cs.LG",
"eess.AS"
] | false |
2305.19709
|
2023-05-31T10:05:33Z
|
XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations
for Text-to-Speech
|
[
"Linh The Nguyen",
"Thinh Pham",
"Dat Quoc Nguyen"
] |
We present XPhoneBERT, the first multilingual model pre-trained to learn
phoneme representations for the downstream text-to-speech (TTS) task. Our
XPhoneBERT has the same model architecture as BERT-base, trained using the
RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100
languages and locales. Experimental results show that employing XPhoneBERT as
an input phoneme encoder significantly boosts the performance of a strong
neural TTS model in terms of naturalness and prosody and also helps produce
fairly high-quality speech with limited training data. We publicly release our
pre-trained XPhoneBERT with the hope that it would facilitate future research
and downstream TTS applications for multiple languages. Our XPhoneBERT model is
available at https://github.com/VinAIResearch/XPhoneBERT
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.19734
|
2023-05-31T10:55:41Z
|
Knowledge Base Question Answering for Space Debris Queries
|
[
"Paul Darm",
"Antonio Valerio Miceli-Barone",
"Shay B. Cohen",
"Annalisa Riccardi"
] |
Space agencies execute complex satellite operations that need to be supported
by the technical knowledge contained in their extensive information systems.
Knowledge bases (KB) are an effective way of storing and accessing such
information at scale. In this work we present a system, developed for the
European Space Agency (ESA), that can answer complex natural language queries,
to support engineers in accessing the information contained in a KB that models
the orbital space debris environment. Our system is based on a pipeline which
first generates a sequence of basic database operations, called a %program
sketch, from a natural language question, then specializes the sketch into a
concrete query program with mentions of entities, attributes and relations, and
finally executes the program against the database. This pipeline decomposition
approach enables us to train the system by leveraging out-of-domain data and
semi-synthetic data generated by GPT-3, thus reducing overfitting and shortcut
learning even with limited amount of in-domain training data. Our code can be
found at \url{https://github.com/PaulDrm/DISCOSQA}.
|
[
"cs.AI",
"cs.CL",
"cs.DB",
"I.2.7"
] | false |
2305.19750
|
2023-05-31T11:33:18Z
|
Text-to-Speech Pipeline for Swiss German -- A comparison
|
[
"Tobias Bollinger",
"Jan Deriu",
"Manfred Vogel"
] |
In this work, we studied the synthesis of Swiss German speech using different
Text-to-Speech (TTS) models. We evaluated the TTS models on three corpora, and
we found, that VITS models performed best, hence, using them for further
testing. We also introduce a new method to evaluate TTS models by letting the
discriminator of a trained vocoder GAN model predict whether a given waveform
is human or synthesized. In summary, our best model delivers speech synthesis
for different Swiss German dialects with previously unachieved quality.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.19761
|
2023-05-31T11:46:13Z
|
Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a
Multi-agent System based on Probabilistic Generative Models
|
[
"Jun Inukai",
"Tadahiro Taniguchi",
"Akira Taniguchi",
"Yoshinobu Hagiwara"
] |
In the studies on symbol emergence and emergent communication in a population
of agents, a computational model was employed in which agents participate in
various language games. Among these, the Metropolis-Hastings naming game (MHNG)
possesses a notable mathematical property: symbol emergence through MHNG is
proven to be a decentralized Bayesian inference of representations shared by
the agents. However, the previously proposed MHNG is limited to a two-agent
scenario. This paper extends MHNG to an N-agent scenario. The main
contributions of this paper are twofold: (1) we propose the recursive
Metropolis-Hastings naming game (RMHNG) as an N-agent version of MHNG and
demonstrate that RMHNG is an approximate Bayesian inference method for the
posterior distribution over a latent variable shared by agents, similar to
MHNG; and (2) we empirically evaluate the performance of RMHNG on synthetic and
real image data, enabling multiple agents to develop and share a symbol system.
Furthermore, we introduce two types of approximations -- one-sample and
limited-length -- to reduce computational complexity while maintaining the
ability to explain communication in a population of agents. The experimental
findings showcased the efficacy of RMHNG as a decentralized Bayesian inference
for approximating the posterior distribution concerning latent variables, which
are jointly shared among agents, akin to MHNG. Moreover, the utilization of
RMHNG elucidated the agents' capacity to exchange symbols. Furthermore, the
study discovered that even the computationally simplified version of RMHNG
could enable symbols to emerge among the agents.
|
[
"cs.CL",
"cs.LG",
"cs.MA"
] | false |
2305.19769
|
2023-05-31T12:00:51Z
|
Attention-Based Methods For Audio Question Answering
|
[
"Parthasaarathy Sudarsanam",
"Tuomas Virtanen"
] |
Audio question answering (AQA) is the task of producing natural language
answers when a system is provided with audio and natural language questions. In
this paper, we propose neural network architectures based on self-attention and
cross-attention for the AQA task. The self-attention layers extract powerful
audio and textual representations. The cross-attention maps audio features that
are relevant to the textual features to produce answers. All our models are
trained on the recently proposed Clotho-AQA dataset for both binary yes/no
questions and single-word answer questions. Our results clearly show
improvement over the reference method reported in the original paper. On the
yes/no binary classification task, our proposed model achieves an accuracy of
68.3% compared to 62.7% in the reference model. For the single-word answers
multiclass classifier, our model produces a top-1 and top-5 accuracy of 57.9%
and 99.8% compared to 54.2% and 93.7% in the reference model respectively. We
further discuss some of the challenges in the Clotho-AQA dataset such as the
presence of the same answer word in multiple tenses, singular and plural forms,
and the presence of specific and generic answers to the same question. We
address these issues and present a revised version of the dataset.
|
[
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2305.19840
|
2023-05-31T13:29:07Z
|
BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish
Language
|
[
"Konrad Wojtasik",
"Vadim Shishkin",
"Kacper Wołowiec",
"Arkadiusz Janz",
"Maciej Piasecki"
] |
The BEIR dataset is a large, heterogeneous benchmark for Information
Retrieval (IR) in zero-shot settings, garnering considerable attention within
the research community. However, BEIR and analogous datasets are predominantly
restricted to the English language. Our objective is to establish extensive
large-scale resources for IR in the Polish language, thereby advancing the
research in this NLP area. In this work, inspired by mMARCO and Mr.~TyDi
datasets, we translated all accessible open IR datasets into Polish, and we
introduced the BEIR-PL benchmark -- a new benchmark which comprises 13
datasets, facilitating further development, training and evaluation of modern
Polish language models for IR tasks. We executed an evaluation and comparison
of numerous IR models on the newly introduced BEIR-PL benchmark. Furthermore,
we publish pre-trained open IR models for Polish language,d marking a
pioneering development in this field. Additionally, the evaluation revealed
that BM25 achieved significantly lower scores for Polish than for English,
which can be attributed to high inflection and intricate morphological
structure of the Polish language. Finally, we trained various re-ranking models
to enhance the BM25 retrieval, and we compared their performance to identify
their unique characteristic features. To ensure accurate model comparisons, it
is necessary to scrutinise individual results rather than to average across the
entire benchmark. Thus, we thoroughly analysed the outcomes of IR models in
relation to each individual data subset encompassed by the BEIR benchmark. The
benchmark data is available at URL {\bf https://huggingface.co/clarin-knext}.
|
[
"cs.IR",
"cs.AI",
"cs.CL"
] | false |
2305.20010
|
2023-05-31T16:32:22Z
|
Human or Not? A Gamified Approach to the Turing Test
|
[
"Daniel Jannai",
"Amos Meron",
"Barak Lenz",
"Yoav Levine",
"Yoav Shoham"
] |
We present "Human or Not?", an online game inspired by the Turing test, that
measures the capability of AI chatbots to mimic humans in dialog, and of humans
to tell bots from other humans. Over the course of a month, the game was played
by over 1.5 million users who engaged in anonymous two-minute chat sessions
with either another human or an AI language model which was prompted to behave
like humans. The task of the players was to correctly guess whether they spoke
to a person or to an AI. This largest scale Turing-style test conducted to date
revealed some interesting facts. For example, overall users guessed the
identity of their partners correctly in only 68% of the games. In the subset of
the games in which users faced an AI bot, users had even lower correct guess
rates of 60% (that is, not much higher than chance). This white paper details
the development, deployment, and results of this unique experiment. While this
experiment calls for many extensions and refinements, these findings already
begin to shed light on the inevitable near future which will commingle humans
and AI.
|
[
"cs.AI",
"cs.CL",
"cs.CY",
"cs.HC",
"68T50",
"I.2.7"
] | true |
2305.20019
|
2023-05-31T16:48:06Z
|
Monotonic Location Attention for Length Generalization
|
[
"Jishnu Ray Chowdhury",
"Cornelia Caragea"
] |
We explore different ways to utilize position-based cross-attention in
seq2seq networks to enable length generalization in algorithmic tasks. We show
that a simple approach of interpolating the original and reversed encoded
representations combined with relative attention allows near-perfect length
generalization for both forward and reverse lookup tasks or copy tasks that had
been generally hard to tackle. We also devise harder diagnostic tasks where the
relative distance of the ideal attention position varies with timestep. In such
settings, the simple interpolation trick with relative attention is not
sufficient. We introduce novel variants of location attention building on top
of Dubois et al. (2020) to address the new diagnostic tasks. We also show the
benefits of our approaches for length generalization in SCAN (Lake & Baroni,
2018) and CFQ (Keysers et al., 2020). Our code is available on GitHub.
|
[
"cs.LG",
"cs.AI",
"cs.CL"
] | false |
2305.20050
|
2023-05-31T17:24:00Z
|
Let's Verify Step by Step
|
[
"Hunter Lightman",
"Vineet Kosaraju",
"Yura Burda",
"Harri Edwards",
"Bowen Baker",
"Teddy Lee",
"Jan Leike",
"John Schulman",
"Ilya Sutskever",
"Karl Cobbe"
] |
In recent years, large language models have greatly improved in their ability
to perform complex multi-step reasoning. However, even state-of-the-art models
still regularly produce logical mistakes. To train more reliable models, we can
turn either to outcome supervision, which provides feedback for a final result,
or process supervision, which provides feedback for each intermediate reasoning
step. Given the importance of training reliable models, and given the high cost
of human feedback, it is important to carefully compare the both methods.
Recent work has already begun this comparison, but many questions still remain.
We conduct our own investigation, finding that process supervision
significantly outperforms outcome supervision for training models to solve
problems from the challenging MATH dataset. Our process-supervised model solves
78% of problems from a representative subset of the MATH test set.
Additionally, we show that active learning significantly improves the efficacy
of process supervision. To support related research, we also release PRM800K,
the complete dataset of 800,000 step-level human feedback labels used to train
our best reward model.
|
[
"cs.LG",
"cs.AI",
"cs.CL"
] | false |
2306.00208
|
2023-05-31T21:58:07Z
|
Strategies for improving low resource speech to text translation relying
on pre-trained ASR models
|
[
"Santosh Kesiraju",
"Marek Sarvas",
"Tomas Pavlicek",
"Cecile Macaire",
"Alejandro Ciuba"
] |
This paper presents techniques and findings for improving the performance of
low-resource speech to text translation (ST). We conducted experiments on both
simulated and real-low resource setups, on language pairs English - Portuguese,
and Tamasheq - French respectively. Using the encoder-decoder framework for ST,
our results show that a multilingual automatic speech recognition system acts
as a good initialization under low-resource scenarios. Furthermore, using the
CTC as an additional objective for translation during training and decoding
helps to reorder the internal representations and improves the final
translation. Through our experiments, we try to identify various factors
(initializations, objectives, and hyper-parameters) that contribute the most
for improvements in low-resource setups. With only 300 hours of pre-training
data, our model achieved 7.3 BLEU score on Tamasheq - French data,
outperforming prior published works from IWSLT 2022 by 1.6 points.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2306.01009
|
2023-05-31T21:29:49Z
|
Examining the Emergence of Deductive Reasoning in Generative Language
Models
|
[
"Peter Belcak",
"Luca A. Lanzendörfer",
"Roger Wattenhofer"
] |
We conduct a preliminary inquiry into the ability of generative transformer
models to deductively reason from premises provided. We observe notable
differences in the performance of models coming from different training setups
and find that the deductive reasoning ability increases with scale. Further, we
discover that the performance generally does not decrease with the length of
the deductive chain needed to reach the conclusion, with the exception of
OpenAI GPT-3 and GPT-3.5 models. Our study considers a wide variety of
transformer-decoder models, ranging from 117 million to 175 billion parameters
in size.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2306.00110
|
2023-05-31T18:34:16Z
|
MuseCoco: Generating Symbolic Music from Text
|
[
"Peiling Lu",
"Xin Xu",
"Chenfei Kang",
"Botao Yu",
"Chengyi Xing",
"Xu Tan",
"Jiang Bian"
] |
Generating music from text descriptions is a user-friendly mode since the
text is a relatively easy interface for user engagement. While some approaches
utilize texts to control music audio generation, editing musical elements in
generated audio is challenging for users. In contrast, symbolic music offers
ease of editing, making it more accessible for users to manipulate specific
musical elements. In this paper, we propose MuseCoco, which generates symbolic
music from text descriptions with musical attributes as the bridge to break
down the task into text-to-attribute understanding and attribute-to-music
generation stages. MuseCoCo stands for Music Composition Copilot that empowers
musicians to generate music directly from given text descriptions, offering a
significant improvement in efficiency compared to creating music entirely from
scratch. The system has two main advantages: Firstly, it is data efficient. In
the attribute-to-music generation stage, the attributes can be directly
extracted from music sequences, making the model training self-supervised. In
the text-to-attribute understanding stage, the text is synthesized and refined
by ChatGPT based on the defined attribute templates. Secondly, the system can
achieve precise control with specific attributes in text descriptions and
offers multiple control options through attribute-conditioned or
text-conditioned approaches. MuseCoco outperforms baseline systems in terms of
musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32
respectively. Besides, there is a notable enhancement of about 20% in objective
control accuracy. In addition, we have developed a robust large-scale model
with 1.2 billion parameters, showcasing exceptional controllability and
musicality.
|
[
"cs.SD",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM",
"eess.AS"
] | true |
2305.19502
|
2023-05-31T02:28:59Z
|
Graph Entropy Minimization for Semi-supervised Node Classification
|
[
"Yi Luo",
"Guangchun Luo",
"Ke Qin",
"Aiguo Chen"
] |
Node classifiers are required to comprehensively reduce prediction errors,
training resources, and inference latency in the industry. However, most graph
neural networks (GNN) concentrate only on one or two of them. The compromised
aspects thus are the shortest boards on the bucket, hindering their practical
deployments for industrial-level tasks. This work proposes a novel
semi-supervised learning method termed Graph Entropy Minimization (GEM) to
resolve the three issues simultaneously. GEM benefits its one-hop aggregation
from massive uncategorized nodes, making its prediction accuracy comparable to
GNNs with two or more hops message passing. It can be decomposed to support
stochastic training with mini-batches of independent edge samples, achieving
extremely fast sampling and space-saving training. While its one-hop
aggregation is faster in inference than deep GNNs, GEM can be further
accelerated to an extreme by deriving a non-hop classifier via online knowledge
distillation. Thus, GEM can be a handy choice for latency-restricted and
error-sensitive services running on resource-constraint hardware. Code is
available at https://github.com/cf020031308/GEM.
|
[
"cs.LG"
] | false |
2305.19636
|
2023-05-31T08:07:35Z
|
Explainable AI for Malnutrition Risk Prediction from m-Health and
Clinical Data
|
[
"Flavio Di Martino",
"Franca Delmastro",
"Cristina Dolciotti"
] |
Malnutrition is a serious and prevalent health problem in the older
population, and especially in hospitalised or institutionalised subjects.
Accurate and early risk detection is essential for malnutrition management and
prevention. M-health services empowered with Artificial Intelligence (AI) may
lead to important improvements in terms of a more automatic, objective, and
continuous monitoring and assessment. Moreover, the latest Explainable AI (XAI)
methodologies may make AI decisions interpretable and trustworthy for end
users. This paper presents a novel AI framework for early and explainable
malnutrition risk detection based on heterogeneous m-health data. We performed
an extensive model evaluation including both subject-independent and
personalised predictions, and the obtained results indicate Random Forest (RF)
and Gradient Boosting as the best performing classifiers, especially when
incorporating body composition assessment data. We also investigated several
benchmark XAI methods to extract global model explanations. Model-specific
explanation consistency assessment indicates that each selected model
privileges similar subsets of the most relevant predictors, with the highest
agreement shown between SHapley Additive ExPlanations (SHAP) and feature
permutation method. Furthermore, we performed a preliminary clinical validation
to verify that the learned feature-output trends are compliant with the current
evidence-based assessment.
|
[
"cs.LG"
] | false |
2305.19717
|
2023-05-31T10:12:23Z
|
Is Rewiring Actually Helpful in Graph Neural Networks?
|
[
"Domenico Tortorella",
"Alessio Micheli"
] |
Graph neural networks compute node representations by performing multiple
message-passing steps that consist in local aggregations of node features.
Having deep models that can leverage longer-range interactions between nodes is
hindered by the issues of over-smoothing and over-squashing. In particular, the
latter is attributed to the graph topology which guides the message-passing,
causing a node representation to become insensitive to information contained at
distant nodes. Many graph rewiring methods have been proposed to remedy or
mitigate this problem. However, properly evaluating the benefits of these
methods is made difficult by the coupling of over-squashing with other issues
strictly related to model training, such as vanishing gradients. Therefore, we
propose an evaluation setting based on message-passing models that do not
require training to compute node and graph representations. We perform a
systematic experimental comparison on real-world node and graph classification
tasks, showing that rewiring the underlying graph rarely does confer a
practical benefit for message-passing.
|
[
"cs.LG"
] | false |
2305.19726
|
2023-05-31T10:36:10Z
|
Learning Representations without Compositional Assumptions
|
[
"Tennison Liu",
"Jeroen Berrevoets",
"Zhaozhi Qian",
"Mihaela van der Schaar"
] |
This paper addresses unsupervised representation learning on tabular data
containing multiple views generated by distinct sources of measurement.
Traditional methods, which tackle this problem using the multi-view framework,
are constrained by predefined assumptions that assume feature sets share the
same information and representations should learn globally shared factors.
However, this assumption is not always valid for real-world tabular datasets
with complex dependencies between feature sets, resulting in localized
information that is harder to learn. To overcome this limitation, we propose a
data-driven approach that learns feature set dependencies by representing
feature sets as graph nodes and their relationships as learnable edges.
Furthermore, we introduce LEGATO, a novel hierarchical graph autoencoder that
learns a smaller, latent graph to aggregate information from multiple views
dynamically. This approach results in latent graph components that specialize
in capturing localized information from different regions of the input, leading
to superior downstream performance.
|
[
"cs.LG"
] | false |
2305.19770
|
2023-05-31T12:03:12Z
|
Quality In / Quality Out: Assessing Data quality in an Anomaly Detection
Benchmark
|
[
"José Camacho",
"Katarzyna Wasielewska",
"Marta Fuentes-García",
"Rafael Rodríguez-Gómez"
] |
Autonomous or self-driving networks are expected to provide a solution to the
myriad of extremely demanding new applications in the Future Internet. The key
to handle complexity is to perform tasks like network optimization and failure
recovery with minimal human supervision. For this purpose, the community relies
on the development of new Machine Learning (ML) models and techniques. However,
ML can only be as good as the data it is fitted with. Datasets provided to the
community as benchmarks for research purposes, which have a relevant impact in
research findings and directions, are often assumed to be of good quality by
default. In this paper, we show that relatively minor modifications on the same
benchmark dataset (UGR'16, a flow-based real-traffic dataset for anomaly
detection) cause significantly more impact on model performance than the
specific ML technique considered. To understand this finding, we contribute a
methodology to investigate the root causes for those differences, and to assess
the quality of the data labelling. Our findings illustrate the need to devote
more attention into (automatic) data quality assessment and optimization
techniques in the context of autonomous networks.
|
[
"cs.LG"
] | false |
2305.19871
|
2023-05-31T14:08:48Z
|
There is more to graphs than meets the eye: Learning universal features
with self-supervision
|
[
"Laya Das",
"Sai Munikoti",
"Mahantesh Halappanavar"
] |
We study the problem of learning universal features across multiple graphs
through self-supervision. Graph self supervised learning has been shown to
facilitate representation learning, and produce competitive models compared to
supervised baselines. However, existing methods of self-supervision learn
features from one graph, and thus, produce models that are specialized to a
particular graph. We hypothesize that leveraging multiple graphs of the same
type/class can improve the quality of learnt representations in the model by
extracting features that are universal to the class of graphs. We adopt a
transformer backbone that acts as a universal representation learning module
for multiple graphs. We leverage neighborhood aggregation coupled with
graph-specific embedding generator to transform disparate node embeddings from
multiple graphs to a common space for the universal backbone. We learn both
universal and graph-specific parameters in an end-to-end manner. Our
experiments reveal that leveraging multiple graphs of the same type -- citation
networks -- improves the quality of representations and results in better
performance on downstream node classification task compared to self-supervision
with one graph. The results of our study improve the state-of-the-art in graph
self-supervised learning, and bridge the gap between self-supervised and
supervised performance.
|
[
"cs.LG"
] | false |
2305.19872
|
2023-05-31T14:09:42Z
|
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative
Polynomials
|
[
"Mingguo He",
"Zhewei Wei",
"Shikun Feng",
"Zhengjie Huang",
"Weibin Li",
"Yu Sun",
"Dianhai Yu"
] |
Heterogeneous Graph Neural Networks (HGNNs) have gained significant
popularity in various heterogeneous graph learning tasks. However, most HGNNs
rely on spatial domain-based message passing and attention modules for
information propagation and aggregation. These spatial-based HGNNs neglect the
utilization of spectral graph convolutions, which are the foundation of Graph
Convolutional Networks (GCN) on homogeneous graphs. Inspired by the
effectiveness and scalability of spectral-based GNNs on homogeneous graphs,
this paper explores the extension of spectral-based GNNs to heterogeneous
graphs. We propose PSHGCN, a novel heterogeneous convolutional network based on
positive noncommutative polynomials. PSHGCN provides a simple yet effective
approach for learning spectral graph convolutions on heterogeneous graphs.
Moreover, we demonstrate the rationale of PSHGCN in graph optimization. We
conducted an extensive experimental study to show that PSHGCN can learn diverse
spectral heterogeneous graph convolutions and achieve superior performance in
node classification tasks. Our code is available at
https://github.com/ivam-he/PSHGCN.
|
[
"cs.LG"
] | false |
2305.19889
|
2023-05-31T14:24:35Z
|
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed
on Orbits
|
[
"Zhuokai Zhao",
"Takumi Matsuzawa",
"William Irvine",
"Michael Maire",
"Gordon L Kindlmann"
] |
Proper evaluations are crucial for better understanding, troubleshooting,
interpreting model behaviors and further improving model performance. While
using scalar-based error metrics provides a fast way to overview model
performance, they are often too abstract to display certain weak spots and lack
information regarding important model properties, such as robustness. This not
only hinders machine learning models from being more interpretable and gaining
trust, but also can be misleading to both model developers and users.
Additionally, conventional evaluation procedures often leave researchers
unclear about where and how model fails, which complicates model comparisons
and further developments. To address these issues, we propose a novel
evaluation workflow, named Non-Equivariance Revealed on Orbits (NERO)
Evaluation. The goal of NERO evaluation is to turn focus from traditional
scalar-based metrics onto evaluating and visualizing models equivariance,
closely capturing model robustness, as well as to allow researchers quickly
investigating interesting or unexpected model behaviors. NERO evaluation is
consist of a task-agnostic interactive interface and a set of visualizations,
called NERO plots, which reveals the equivariance property of the model. Case
studies on how NERO evaluation can be applied to multiple research areas,
including 2D digit recognition, object detection, particle image velocimetry
(PIV), and 3D point cloud classification, demonstrate that NERO evaluation can
quickly illustrate different model equivariance, and effectively explain model
behaviors through interactive visualizations of the model outputs. In addition,
we propose consensus, an alternative to ground truths, to be used in NERO
evaluation so that model equivariance can still be evaluated with new,
unlabeled datasets.
|
[
"cs.LG"
] | false |
2306.00035
|
2023-05-31T08:33:23Z
|
ROSARL: Reward-Only Safe Reinforcement Learning
|
[
"Geraud Nangue Tasse",
"Tamlin Love",
"Mark Nemecek",
"Steven James",
"Benjamin Rosman"
] |
An important problem in reinforcement learning is designing agents that learn
to solve tasks safely in an environment. A common solution is for a human
expert to define either a penalty in the reward function or a cost to be
minimised when reaching unsafe states. However, this is non-trivial, since too
small a penalty may lead to agents that reach unsafe states, while too large a
penalty increases the time to convergence. Additionally, the difficulty in
designing reward or cost functions can increase with the complexity of the
problem. Hence, for a given environment with a given set of unsafe states, we
are interested in finding the upper bound of rewards at unsafe states whose
optimal policies minimise the probability of reaching those unsafe states,
irrespective of task rewards. We refer to this exact upper bound as the "Minmax
penalty", and show that it can be obtained by taking into account both the
controllability and diameter of an environment. We provide a simple practical
model-free algorithm for an agent to learn this Minmax penalty while learning
the task policy, and demonstrate that using it leads to agents that learn safe
policies in high-dimensional continuous control environments.
|
[
"cs.LG"
] | false |
2306.00152
|
2023-05-31T19:50:11Z
|
Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for
Semi-Supervised Learning on Multilayer Graphs
|
[
"Sara Venturini",
"Andrea Cristofari",
"Francesco Rinaldi",
"Francesco Tudisco"
] |
Clustering (or community detection) on multilayer graphs poses several
additional complications with respect to standard graphs as different layers
may be characterized by different structures and types of information. One of
the major challenges is to establish the extent to which each layer contributes
to the cluster assignment in order to effectively take advantage of the
multilayer structure and improve upon the classification obtained using the
individual layers or their union. However, making an informed a-priori
assessment about the clustering information content of the layers can be very
complicated. In this work, we assume a semi-supervised learning setting, where
the class of a small percentage of nodes is initially provided, and we propose
a parameter-free Laplacian-regularized model that learns an optimal nonlinear
combination of the different layers from the available input labels. The
learning algorithm is based on a Frank-Wolfe optimization scheme with inexact
gradient, combined with a modified Label Propagation iteration. We provide a
detailed convergence analysis of the algorithm and extensive experiments on
synthetic and real-world datasets, showing that the proposed method compares
favourably with a variety of baselines and outperforms each individual layer
when used in isolation.
|
[
"cs.LG"
] | false |
2306.00172
|
2023-05-31T20:41:42Z
|
Learning for Edge-Weighted Online Bipartite Matching with Robustness
Guarantees
|
[
"Pengfei Li",
"Jianyi Yang",
"Shaolei Ren"
] |
Many problems, such as online ad display, can be formulated as online
bipartite matching. The crucial challenge lies in the nature of
sequentially-revealed online item information, based on which we make
irreversible matching decisions at each step. While numerous expert online
algorithms have been proposed with bounded worst-case competitive ratios, they
may not offer satisfactory performance in average cases. On the other hand,
reinforcement learning (RL) has been applied to improve the average
performance, but it lacks robustness and can perform arbitrarily poorly. In
this paper, we propose a novel RL-based approach to edge-weighted online
bipartite matching with robustness guarantees (LOMAR), achieving both good
average-case and worst-case performance. The key novelty of LOMAR is a new
online switching operation which, based on a judicious condition to hedge
against future uncertainties, decides whether to follow the expert's decision
or the RL decision for each online item. We prove that for any $\rho\in[0,1]$,
LOMAR is $\rho$-competitive against any given expert online algorithm. To
improve the average performance, we train the RL policy by explicitly
considering the online switching operation. Finally, we run empirical
experiments to demonstrate the advantages of LOMAR compared to existing
baselines. Our code is available at: https://github.com/Ren-Research/LOMAR
|
[
"cs.LG"
] | false |
2305.19476
|
2023-05-31T01:09:28Z
|
Accelerating Reinforcement Learning with Value-Conditional State Entropy
Exploration
|
[
"Dongyoung Kim",
"Jinwoo Shin",
"Pieter Abbeel",
"Younggyo Seo"
] |
A promising technique for exploration is to maximize the entropy of visited
state distribution, i.e., state entropy, by encouraging uniform coverage of
visited state space. While it has been effective for an unsupervised setup, it
tends to struggle in a supervised setup with a task reward, where an agent
prefers to visit high-value states to exploit the task reward. Such a
preference can cause an imbalance between the distributions of high-value
states and low-value states, which biases exploration towards low-value state
regions as a result of the state entropy increasing when the distribution
becomes more uniform. This issue is exacerbated when high-value states are
narrowly distributed within the state space, making it difficult for the agent
to complete the tasks. In this paper, we present a novel exploration technique
that maximizes the value-conditional state entropy, which separately estimates
the state entropies that are conditioned on the value estimates of each state,
then maximizes their average. By only considering the visited states with
similar value estimates for computing the intrinsic bonus, our method prevents
the distribution of low-value states from affecting exploration around
high-value states, and vice versa. We demonstrate that the proposed alternative
to the state entropy baseline significantly accelerates various reinforcement
learning algorithms across a variety of tasks within MiniGrid, DeepMind Control
Suite, and Meta-World benchmarks. Source code is available at
https://sites.google.com/view/rl-vcse.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19499
|
2023-05-31T02:16:53Z
|
Deep into The Domain Shift: Transfer Learning through Dependence
Regularization
|
[
"Shumin Ma",
"Zhiri Yuan",
"Qi Wu",
"Yiyan Huang",
"Xixu Hu",
"Cheuk Hang Leung",
"Dongdong Wang",
"Zhixiang Huang"
] |
Classical Domain Adaptation methods acquire transferability by regularizing
the overall distributional discrepancies between features in the source domain
(labeled) and features in the target domain (unlabeled). They often do not
differentiate whether the domain differences come from the marginals or the
dependence structures. In many business and financial applications, the
labeling function usually has different sensitivities to the changes in the
marginals versus changes in the dependence structures. Measuring the overall
distributional differences will not be discriminative enough in acquiring
transferability. Without the needed structural resolution, the learned transfer
is less optimal. This paper proposes a new domain adaptation approach in which
one can measure the differences in the internal dependence structure separately
from those in the marginals. By optimizing the relative weights among them, the
new regularization strategy greatly relaxes the rigidness of the existing
approaches. It allows a learning machine to pay special attention to places
where the differences matter the most. Experiments on three real-world datasets
show that the improvements are quite notable and robust compared to various
benchmark domain adaptation models.
|
[
"cs.LG",
"q-fin.CP"
] | false |
2305.19531
|
2023-05-31T03:36:50Z
|
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
|
[
"Zhaocheng Liu",
"Zhongxiang Fan",
"Jian Liang",
"Dongying Kong",
"Han Li"
] |
The one-epoch overfitting phenomenon has been widely observed in industrial
Click-Through Rate (CTR) applications, where the model performance experiences
a significant degradation at the beginning of the second epoch. Recent advances
try to understand the underlying factors behind this phenomenon through
extensive experiments. However, it is still unknown whether a multi-epoch
training paradigm could achieve better results, as the best performance is
usually achieved by one-epoch training. In this paper, we hypothesize that the
emergence of this phenomenon may be attributed to the susceptibility of the
embedding layer to overfitting, which can stem from the high-dimensional
sparsity of data. To maintain feature sparsity while simultaneously avoiding
overfitting of embeddings, we propose a novel Multi-Epoch learning with Data
Augmentation (MEDA), which can be directly applied to most deep CTR models.
MEDA achieves data augmentation by reinitializing the embedding layer in each
epoch, thereby avoiding embedding overfitting and simultaneously improving
convergence. To our best knowledge, MEDA is the first multi-epoch training
paradigm designed for deep CTR prediction models. We conduct extensive
experiments on several public datasets, and the effectiveness of our proposed
MEDA is fully verified. Notably, the results show that MEDA can significantly
outperform the conventional one-epoch training. Besides, MEDA has exhibited
significant benefits in a real-world scene on Kuaishou.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.19570
|
2023-05-31T05:39:52Z
|
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
|
[
"Dheeraj Baby",
"Saurabh Garg",
"Tzu-Ching Yen",
"Sivaraman Balakrishnan",
"Zachary Chase Lipton",
"Yu-Xiang Wang"
] |
This paper focuses on supervised and unsupervised online label shift, where
the class marginals $Q(y)$ varies but the class-conditionals $Q(x|y)$ remain
invariant. In the unsupervised setting, our goal is to adapt a learner, trained
on some offline labeled data, to changing label distributions given unlabeled
online data. In the supervised setting, we must both learn a classifier and
adapt to the dynamically evolving class marginals given only labeled online
data. We develop novel algorithms that reduce the adaptation problem to online
regression and guarantee optimal dynamic regret without any prior knowledge of
the extent of drift in the label distribution. Our solution is based on
bootstrapping the estimates of \emph{online regression oracles} that track the
drifting proportions. Experiments across numerous simulated and real-world
online label shift scenarios demonstrate the superior performance of our
proposed approaches, often achieving 1-3\% improvement in accuracy while being
sample and computationally efficient. Code is publicly available at
https://github.com/acmi-lab/OnlineLabelShift.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19593
|
2023-05-31T06:31:42Z
|
Exploring the Vulnerabilities of Machine Learning and Quantum Machine
Learning to Adversarial Attacks using a Malware Dataset: A Comparative
Analysis
|
[
"Mst Shapna Akter",
"Hossain Shahriar",
"Iysa Iqbal",
"MD Hossain",
"M. A. Karim",
"Victor Clincy",
"Razvan Voicu"
] |
The burgeoning fields of machine learning (ML) and quantum machine learning
(QML) have shown remarkable potential in tackling complex problems across
various domains. However, their susceptibility to adversarial attacks raises
concerns when deploying these systems in security sensitive applications. In
this study, we present a comparative analysis of the vulnerability of ML and
QML models, specifically conventional neural networks (NN) and quantum neural
networks (QNN), to adversarial attacks using a malware dataset. We utilize a
software supply chain attack dataset known as ClaMP and develop two distinct
models for QNN and NN, employing Pennylane for quantum implementations and
TensorFlow and Keras for traditional implementations. Our methodology involves
crafting adversarial samples by introducing random noise to a small portion of
the dataset and evaluating the impact on the models performance using accuracy,
precision, recall, and F1 score metrics. Based on our observations, both ML and
QML models exhibit vulnerability to adversarial attacks. While the QNNs
accuracy decreases more significantly compared to the NN after the attack, it
demonstrates better performance in terms of precision and recall, indicating
higher resilience in detecting true positives under adversarial conditions. We
also find that adversarial samples crafted for one model type can impair the
performance of the other, highlighting the need for robust defense mechanisms.
Our study serves as a foundation for future research focused on enhancing the
security and resilience of ML and QML models, particularly QNN, given its
recent advancements. A more extensive range of experiments will be conducted to
better understand the performance and robustness of both models in the face of
adversarial attacks.
|
[
"cs.LG",
"quant-ph"
] | false |
2305.19617
|
2023-05-31T07:36:11Z
|
MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap
Mixup
|
[
"Mao Ye",
"Haitao Wang",
"Zheqian Chen"
] |
To solve the problem of poor performance of deep neural network models due to
insufficient data, a simple yet effective interpolation-based data augmentation
method is proposed: MSMix (Manifold Swap Mixup). This method feeds two
different samples to the same deep neural network model, and then randomly
select a specific layer and partially replace hidden features at that layer of
one of the samples by the counterpart of the other. The mixed hidden features
are fed to the model and go through the rest of the network. Two different
selection strategies are also proposed to obtain richer hidden representation.
Experiments are conducted on three Chinese intention recognition datasets, and
the results show that the MSMix method achieves better results than other
methods in both full-sample and small-sample configurations.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19640
|
2023-05-31T08:13:14Z
|
Optimal Estimates for Pairwise Learning with Deep ReLU Networks
|
[
"Junyu Zhou",
"Shuo Huang",
"Han Feng",
"Ding-Xuan Zhou"
] |
Pairwise learning refers to learning tasks where a loss takes a pair of
samples into consideration. In this paper, we study pairwise learning with deep
ReLU networks and estimate the excess generalization error. For a general loss
satisfying some mild conditions, a sharp bound for the estimation error of
order $O((V\log(n) /n)^{1/(2-\beta)})$ is established. In particular, with the
pairwise least squares loss, we derive a nearly optimal bound of the excess
generalization error which achieves the minimax lower bound up to a logrithmic
term when the true predictor satisfies some smoothness regularities.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19674
|
2023-05-31T09:15:39Z
|
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis
|
[
"Gábor Lugosi",
"Gergely Neu"
] |
We present a new framework for deriving bounds on the generalization bound of
statistical learning algorithms from the perspective of online learning.
Specifically, we construct an online learning game called the "generalization
game", where an online learner is trying to compete with a fixed statistical
learning algorithm in predicting the sequence of generalization gaps on a
training set of i.i.d. data points. We establish a connection between the
online and statistical learning setting by showing that the existence of an
online learning algorithm with bounded regret in this game implies a bound on
the generalization error of the statistical learning algorithm, up to a
martingale concentration term that is independent of the complexity of the
statistical learning method. This technique allows us to recover several
standard generalization bounds including a range of PAC-Bayesian and
information-theoretic guarantees, as well as generalizations thereof.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19691
|
2023-05-31T09:35:03Z
|
Constant or logarithmic regret in asynchronous multiplayer bandits
|
[
"Hugo Richard",
"Etienne Boursier",
"Vianney Perchet"
] |
Multiplayer bandits have recently been extensively studied because of their
application to cognitive radio networks.
While the literature mostly considers synchronous players, radio networks
(e.g. for IoT) tend to have asynchronous devices. This motivates the harder,
asynchronous multiplayer bandits problem, which was first tackled with an
explore-then-commit (ETC) algorithm (see Dakdouk, 2022), with a regret
upper-bound in $\mathcal{O}(T^{\frac{2}{3}})$. Before even considering
decentralization, understanding the centralized case was still a challenge as
it was unknown whether getting a regret smaller than $\Omega(T^{\frac{2}{3}})$
was possible.
We answer positively this question, as a natural extension of UCB exhibits a
$\mathcal{O}(\sqrt{T\log(T)})$ minimax regret.
More importantly, we introduce Cautious Greedy, a centralized algorithm that
yields constant instance-dependent regret if the optimal policy assigns at
least one player on each arm (a situation that is proved to occur when arm
means are close enough). Otherwise, its regret increases as the sum of
$\log(T)$ over some sub-optimality gaps. We provide lower bounds showing that
Cautious Greedy is optimal in the data-dependent terms.
Therefore, we set up a strong baseline for asynchronous multiplayer bandits
and suggest that learning the optimal policy in this problem might be easier
than thought, at least with centralization.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.19727
|
2023-05-31T10:39:51Z
|
Unbalanced Low-rank Optimal Transport Solvers
|
[
"Meyer Scetbon",
"Michal Klein",
"Giovanni Palla",
"Marco Cuturi"
] |
The relevance of optimal transport methods to machine learning has long been
hindered by two salient limitations. First, the $O(n^3)$ computational cost of
standard sample-based solvers (when used on batches of $n$ samples) is
prohibitive. Second, the mass conservation constraint makes OT solvers too
rigid in practice: because they must match \textit{all} points from both
measures, their output can be heavily influenced by outliers. A flurry of
recent works in OT has addressed these computational and modelling limitations,
but has resulted in two separate strains of methods: While the computational
outlook was much improved by entropic regularization, more recent $O(n)$
linear-time \textit{low-rank} solvers hold the promise to scale up OT further.
On the other hand, modelling rigidities have been eased owing to unbalanced
variants of OT, that rely on penalization terms to promote, rather than impose,
mass conservation. The goal of this paper is to merge these two strains, to
achieve the promise of \textit{both} versatile/scalable unbalanced/low-rank OT
solvers. We propose custom algorithms to implement these extensions for the
linear OT problem and its Fused-Gromov-Wasserstein generalization, and
demonstrate their practical relevance to challenging spatial transcriptomics
matching problems.
|
[
"cs.LG",
"math.OC"
] | false |
2305.19744
|
2023-05-31T11:10:29Z
|
Neural Markov Jump Processes
|
[
"Patrick Seifner",
"Ramses J. Sanchez"
] |
Markov jump processes are continuous-time stochastic processes with a wide
range of applications in both natural and social sciences. Despite their
widespread use, inference in these models is highly non-trivial and typically
proceeds via either Monte Carlo or expectation-maximization methods. In this
work we introduce an alternative, variational inference algorithm for Markov
jump processes which relies on neural ordinary differential equations, and is
trainable via back-propagation. Our methodology learns neural, continuous-time
representations of the observed data, that are used to approximate the initial
distribution and time-dependent transition probability rates of the posterior
Markov jump process. The time-independent rates of the prior process are in
contrast trained akin to generative adversarial networks. We test our approach
on synthetic data sampled from ground-truth Markov jump processes, experimental
switching ion channel data and molecular dynamics simulations. Source code to
reproduce our experiments is available online.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.19802
|
2023-05-31T12:41:20Z
|
Neuro-Causal Factor Analysis
|
[
"Alex Markham",
"Mingyu Liu",
"Bryon Aragam",
"Liam Solus"
] |
Factor analysis (FA) is a statistical tool for studying how observed
variables with some mutual dependences can be expressed as functions of
mutually independent unobserved factors, and it is widely applied throughout
the psychological, biological, and physical sciences. We revisit this classic
method from the comparatively new perspective given by advancements in causal
discovery and deep learning, introducing a framework for Neuro-Causal Factor
Analysis (NCFA). Our approach is fully nonparametric: it identifies factors via
latent causal discovery methods and then uses a variational autoencoder (VAE)
that is constrained to abide by the Markov factorization of the distribution
with respect to the learned graph. We evaluate NCFA on real and synthetic data
sets, finding that it performs comparably to standard VAEs on data
reconstruction tasks but with the advantages of sparser architecture, lower
model complexity, and causal interpretability. Unlike traditional FA methods,
our proposed NCFA method allows learning and reasoning about the latent factors
underlying observed data from a justifiably causal perspective, even when the
relations between factors and measurements are highly nonlinear.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19804
|
2023-05-31T12:45:55Z
|
Distance Rank Score: Unsupervised filter method for feature selection on
imbalanced dataset
|
[
"Katarina Firdova",
"Céline Labart",
"Arthur Martel"
] |
This paper presents a new filter method for unsupervised feature selection.
This method is particularly effective on imbalanced multi-class dataset, as in
case of clusters of different anomaly types. Existing methods usually involve
the variance of the features, which is not suitable when the different types of
observations are not represented equally. Our method, based on Spearman's Rank
Correlation between distances on the observations and on feature values, avoids
this drawback. The performance of the method is measured on several clustering
problems and is compared with existing filter methods suitable for unsupervised
data.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19831
|
2023-05-31T13:16:07Z
|
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource
Allocation and Heterogeneity
|
[
"Kok-Seng Wong",
"Manh Nguyen-Duc",
"Khiem Le-Huy",
"Long Ho-Tuan",
"Cuong Do-Danh",
"Danh Le-Phuoc"
] |
Nowadays, billions of phones, IoT and edge devices around the world generate
data continuously, enabling many Machine Learning (ML)-based products and
applications. However, due to increasing privacy concerns and regulations,
these data tend to reside on devices (clients) instead of being centralized for
performing traditional ML model training. Federated Learning (FL) is a
distributed approach in which a single server and multiple clients
collaboratively build an ML model without moving data away from clients.
Whereas existing studies on FL have their own experimental evaluations, most
experiments were conducted using a simulation setting or a small-scale testbed.
This might limit the understanding of FL implementation in realistic
environments. In this empirical study, we systematically conduct extensive
experiments on a large network of IoT and edge devices (called IoT-Edge
devices) to present FL real-world characteristics, including learning
performance and operation (computation and communication) costs. Moreover, we
mainly concentrate on heterogeneous scenarios, which is the most challenging
issue of FL. By investigating the feasibility of on-device implementation, our
study provides valuable insights for researchers and practitioners, promoting
the practicality of FL and assisting in improving the current design of real FL
systems.
|
[
"cs.LG",
"cs.DC"
] | false |
2305.19837
|
2023-05-31T13:25:26Z
|
EAMDrift: An interpretable self retrain model for time series
|
[
"Gonçalo Mateus",
"Cláudia Soares",
"João Leitão",
"António Rodrigues"
] |
The use of machine learning for time series prediction has become
increasingly popular across various industries thanks to the availability of
time series data and advancements in machine learning algorithms. However,
traditional methods for time series forecasting rely on pre-optimized models
that are ill-equipped to handle unpredictable patterns in data. In this paper,
we present EAMDrift, a novel method that combines forecasts from multiple
individual predictors by weighting each prediction according to a performance
metric. EAMDrift is designed to automatically adapt to out-of-distribution
patterns in data and identify the most appropriate models to use at each moment
through interpretable mechanisms, which include an automatic retraining
process. Specifically, we encode different concepts with different models, each
functioning as an observer of specific behaviors. The activation of the overall
model then identifies which subset of the concept observers is identifying
concepts in the data. This activation is interpretable and based on learned
rules, allowing to study of input variables relations. Our study on real-world
datasets shows that EAMDrift outperforms individual baseline models by 20% and
achieves comparable accuracy results to non-interpretable ensemble models.
These findings demonstrate the efficacy of EAMDrift for time-series prediction
and highlight the importance of interpretability in machine learning models.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.19864
|
2023-05-31T13:57:56Z
|
Designing Closed-Loop Models for Task Allocation
|
[
"Vijay Keswani",
"L. Elisa Celis",
"Krishnaram Kenthapadi",
"Matthew Lease"
] |
Automatically assigning tasks to people is challenging because human
performance can vary across tasks for many reasons. This challenge is further
compounded in real-life settings in which no oracle exists to assess the
quality of human decisions and task assignments made. Instead, we find
ourselves in a "closed" decision-making loop in which the same fallible human
decisions we rely on in practice must also be used to guide task allocation.
How can imperfect and potentially biased human decisions train an accurate
allocation model? Our key insight is to exploit weak prior information on
human-task similarity to bootstrap model training. We show that the use of such
a weak prior can improve task allocation accuracy, even when human
decision-makers are fallible and biased. We present both theoretical analysis
and empirical evaluation over synthetic data and a social media toxicity
detection task. Results demonstrate the efficacy of our approach.
|
[
"cs.HC",
"cs.LG"
] | false |
2305.19901
|
2023-05-31T14:32:26Z
|
Adaptive Conformal Regression with Jackknife+ Rescaled Scores
|
[
"Nicolas Deutschmann",
"Mattia Rigotti",
"Maria Rodriguez Martinez"
] |
Conformal regression provides prediction intervals with global coverage
guarantees, but often fails to capture local error distributions, leading to
non-homogeneous coverage. We address this with a new adaptive method based on
rescaling conformal scores with an estimate of local score distribution,
inspired by the Jackknife+ method, which enables the use of calibration data in
conformal scores without breaking calibration-test exchangeability. Our
approach ensures formal global coverage guarantees and is supported by new
theoretical results on local coverage, including an a posteriori bound on any
calibration score. The strength of our approach lies in achieving local
coverage without sacrificing calibration set size, improving the applicability
of conformal prediction intervals in various settings. As a result, our method
provides prediction intervals that outperform previous methods, particularly in
the low-data regime, making it especially relevant for real-world applications
such as healthcare and biomedical domains where uncertainty needs to be
quantified accurately despite low sample data.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.19923
|
2023-05-31T15:01:38Z
|
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
|
[
"Fei Ni",
"Jianye Hao",
"Yao Mu",
"Yifu Yuan",
"Yan Zheng",
"Bin Wang",
"Zhixuan Liang"
] |
Recently, diffusion model shines as a promising backbone for the sequence
modeling paradigm in offline reinforcement learning(RL). However, these works
mostly lack the generalization ability across tasks with reward or dynamics
change. To tackle this challenge, in this paper we propose a task-oriented
conditioned diffusion planner for offline meta-RL(MetaDiffuser), which
considers the generalization problem as conditional trajectory generation task
with contextual representation. The key is to learn a context conditioned
diffusion model which can generate task-oriented trajectories for planning
across diverse tasks. To enhance the dynamics consistency of the generated
trajectories while encouraging trajectories to achieve high returns, we further
design a dual-guided module in the sampling process of the diffusion model. The
proposed framework enjoys the robustness to the quality of collected warm-start
data from the testing task and the flexibility to incorporate with different
task representation method. The experiment results on MuJoCo benchmarks show
that MetaDiffuser outperforms other strong offline meta-RL baselines,
demonstrating the outstanding conditional generation ability of diffusion
architecture.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19982
|
2023-05-31T16:06:50Z
|
Adam Accumulation to Reduce Memory Footprints of both Activations and
Gradients for Large-scale DNN Training
|
[
"Yijia Zhang",
"Yibo Han",
"Shijie Cao",
"Guohao Dai",
"Youshan Miao",
"Ting Cao",
"Fan Yang",
"Ningyi Xu"
] |
Running out of GPU memory has become a main bottleneck for large-scale DNN
training. How to reduce the memory footprint during training has received
intensive research attention. We find that previous gradient accumulation
reduces activation memory but fails to be compatible with gradient memory
reduction due to a contradiction between preserving gradients and releasing
gradients. To address this issue, we propose a novel optimizer accumulation
method for Adam, named Adam Accumulation (AdamA), which enables reducing both
activation and gradient memory. Specifically, AdamA directly integrates
gradients into optimizer states and accumulates optimizer states over
micro-batches, so that gradients can be released immediately after use. We
mathematically and experimentally demonstrate AdamA yields the same convergence
properties as Adam. Evaluated on transformer-based models, AdamA achieves up to
23% memory reduction compared to gradient accumulation with less than 2%
degradation in training throughput. Notably, AdamA can work together with
memory reduction methods for optimizer states to fit 1.26x~3.14x larger models
over PyTorch and DeepSpeed baseline on GPUs with different memory capacities.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.19992
|
2023-05-31T16:13:46Z
|
A Nested Matrix-Tensor Model for Noisy Multi-view Clustering
|
[
"Mohamed El Amine Seddik",
"Mastane Achab",
"Henrique Goulart",
"Merouane Debbah"
] |
In this paper, we propose a nested matrix-tensor model which extends the
spiked rank-one tensor model of order three. This model is particularly
motivated by a multi-view clustering problem in which multiple noisy
observations of each data point are acquired, with potentially non-uniform
variances along the views. In this case, data can be naturally represented by
an order-three tensor where the views are stacked. Given such a tensor, we
consider the estimation of the hidden clusters via performing a best rank-one
tensor approximation. In order to study the theoretical performance of this
approach, we characterize the behavior of this best rank-one approximation in
terms of the alignments of the obtained component vectors with the hidden model
parameter vectors, in the large-dimensional regime. In particular, we show that
our theoretical results allow us to anticipate the exact accuracy of the
proposed clustering approach. Furthermore, numerical experiments indicate that
leveraging our tensor-based approach yields better accuracy compared to a naive
unfolding-based algorithm which ignores the underlying low-rank tensor
structure. Our analysis unveils unexpected and non-trivial phase transition
phenomena depending on the model parameters, ``interpolating'' between the
typical behavior observed for the spiked matrix and tensor models.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.20011
|
2023-05-31T16:34:58Z
|
Constrained Causal Bayesian Optimization
|
[
"Virginia Aglietti",
"Alan Malek",
"Ira Ktena",
"Silvia Chiappa"
] |
We propose constrained causal Bayesian optimization (cCBO), an approach for
finding interventions in a known causal graph that optimize a target variable
under some constraints. cCBO first reduces the search space by exploiting the
graph structure and, if available, an observational dataset; and then solves
the restricted optimization problem by modelling target and constraint
quantities using Gaussian processes and by sequentially selecting interventions
via a constrained expected improvement acquisition function. We propose
different surrogate models that enable to integrate observational and
interventional data while capturing correlation among effects with increasing
levels of sophistication. We evaluate cCBO on artificial and real-world causal
graphs showing successful trade off between fast convergence and percentage of
feasible interventions.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.20028
|
2023-05-31T17:00:00Z
|
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
|
[
"Yucen Lily Li",
"Tim G. J. Rudner",
"Andrew Gordon Wilson"
] |
Bayesian optimization is a highly efficient approach to optimizing objective
functions which are expensive to query. These objectives are typically
represented by Gaussian process (GP) surrogate models which are easy to
optimize and support exact inference. While standard GP surrogates have been
well-established in Bayesian optimization, Bayesian neural networks (BNNs) have
recently become practical function approximators, with many benefits over
standard GPs such as the ability to naturally handle non-stationarity and learn
representations for high-dimensional data. In this paper, we study BNNs as
alternatives to standard GP surrogates for optimization. We consider a variety
of approximate inference procedures for finite-width BNNs, including
high-quality Hamiltonian Monte Carlo, low-cost stochastic MCMC, and heuristics
such as deep ensembles. We also consider infinite-width BNNs and partially
stochastic models such as deep kernel learning. We evaluate this collection of
surrogate models on diverse problems with varying dimensionality, number of
objectives, non-stationarity, and discrete and continuous inputs. We find: (i)
the ranking of methods is highly problem dependent, suggesting the need for
tailored inductive biases; (ii) HMC is the most successful approximate
inference procedure for fully stochastic BNNs; (iii) full stochasticity may be
unnecessary as deep kernel learning is relatively competitive; (iv)
infinite-width BNNs are particularly promising, especially in high dimensions.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.20043
|
2023-05-31T17:14:20Z
|
Deception by Omission: Using Adversarial Missingness to Poison Causal
Structure Learning
|
[
"Deniz Koyuncu",
"Alex Gittens",
"Bülent Yener",
"Moti Yung"
] |
Inference of causal structures from observational data is a key component of
causal machine learning; in practice, this data may be incompletely observed.
Prior work has demonstrated that adversarial perturbations of completely
observed training data may be used to force the learning of inaccurate causal
structural models (SCMs). However, when the data can be audited for correctness
(e.g., it is crytographically signed by its source), this adversarial mechanism
is invalidated. This work introduces a novel attack methodology wherein the
adversary deceptively omits a portion of the true training data to bias the
learned causal structures in a desired manner. Theoretically sound attack
mechanisms are derived for the case of arbitrary SCMs, and a sample-efficient
learning-based heuristic is given for Gaussian SCMs. Experimental validation of
these approaches on real and synthetic data sets demonstrates the effectiveness
of adversarial missingness attacks at deceiving popular causal structure
learning algorithms.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.20056
|
2023-05-31T17:29:24Z
|
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
|
[
"Arvind Pillai",
"Subigya Nepal",
"Andrew Campbell"
] |
Rare life events significantly impact mental health, and their detection in
behavioral studies is a crucial step towards health-based interventions. We
envision that mobile sensing data can be used to detect these anomalies.
However, the human-centered nature of the problem, combined with the
infrequency and uniqueness of these events makes it challenging for
unsupervised machine learning methods. In this paper, we first investigate
granger-causality between life events and human behavior using sensing data.
Next, we propose a multi-task framework with an unsupervised autoencoder to
capture irregular behavior, and an auxiliary sequence predictor that identifies
transitions in workplace performance to contextualize events. We perform
experiments using data from a mobile sensing study comprising N=126 information
workers from multiple industries, spanning 10106 days with 198 rare events
(<2%). Through personalized inference, we detect the exact day of a rare event
with an F1 of 0.34, demonstrating that our method outperforms several
baselines. Finally, we discuss the implications of our work from the context of
real-world deployment.
|
[
"cs.LG",
"cs.HC"
] | false |
2305.20068
|
2023-05-31T17:43:56Z
|
TOFG: A Unified and Fine-Grained Environment Representation in
Autonomous Driving
|
[
"Zihao Wen",
"Yifan Zhang",
"Xinhong Chen",
"Jianping Wang"
] |
In autonomous driving, an accurate understanding of environment, e.g., the
vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in
many driving tasks such as trajectory prediction and motion planning.
Environment information comes from high-definition (HD) map and historical
trajectories of vehicles. Due to the heterogeneity of the map data and
trajectory data, many data-driven models for trajectory prediction and motion
planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a
separate and sequential manner. However, such a manner may capture biased
interpretation of interactions, causing lower prediction and planning accuracy.
Moreover, separate extraction leads to a complicated model structure and hence
the overall efficiency and scalability are sacrificed. To address the above
issues, we propose an environment representation, Temporal Occupancy Flow Graph
(TOFG). Specifically, the occupancy flow-based representation unifies the map
information and vehicle trajectories into a homogeneous data format and enables
a consistent prediction. The temporal dependencies among vehicles can help
capture the change of occupancy flow timely to further promote model
performance. To demonstrate that TOFG is capable of simplifying the model
architecture, we incorporate TOFG with a simple graph attention (GAT) based
neural network and propose TOFG-GAT, which can be used for both trajectory
prediction and motion planning. Experiment results show that TOFG-GAT achieves
better or competitive performance than all the SOTA baselines with less
training time.
|
[
"cs.RO",
"cs.LG"
] | false |
2305.20072
|
2023-05-31T17:46:14Z
|
Alternating Minimization for Regression with Tropical Rational Functions
|
[
"Alex Dunbar",
"Lars Ruthotto"
] |
We propose an alternating minimization heuristic for regression over the
space of tropical rational functions with fixed exponents. The method
alternates between fitting the numerator and denominator terms via tropical
polynomial regression, which is known to admit a closed form solution. We
demonstrate the behavior of the alternating minimization method experimentally.
Experiments demonstrate that the heuristic provides a reasonable approximation
of the input data. Our work is motivated by applications to ReLU neural
networks, a popular class of network architectures in the machine learning
community which are closely related to tropical rational functions.
|
[
"math.OC",
"cs.LG",
"90C24, 14T90, 62J02"
] | false |
2306.00026
|
2023-05-31T02:21:11Z
|
Efficient Stochastic Approximation of Minimax Excess Risk Optimization
|
[
"Lijun Zhang",
"Wei-Wei Tu"
] |
While traditional distributionally robust optimization (DRO) aims to minimize
the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently
proposed a variant that replaces risk with excess risk. Compared to DRO, the
new formulation -- minimax excess risk optimization (MERO) has the advantage of
suppressing the effect of heterogeneous noise in different distributions.
However, the choice of excess risk leads to a very challenging minimax
optimization problem, and currently there exists only an inefficient algorithm
for empirical MERO. In this paper, we develop efficient stochastic
approximation approaches which directly target MERO. Specifically, we leverage
techniques from stochastic convex optimization to estimate the minimal risk of
every distribution, and solve MERO as a stochastic convex-concave optimization
(SCCO) problem with biased gradients. The presence of bias makes existing
theoretical guarantees of SCCO inapplicable, and fortunately, we demonstrate
that the bias, caused by the estimation error of the minimal risk, is
under-control. Thus, MERO can still be optimized with a nearly optimal
convergence rate. Moreover, we investigate a practical scenario where the
quantity of samples drawn from each distribution may differ, and propose a
stochastic approach that delivers distribution-dependent convergence rates.
|
[
"math.OC",
"cs.LG"
] | false |
2306.00096
|
2023-05-31T18:15:09Z
|
Pareto Front Identification with Regret Minimization
|
[
"Wonyoung Kim",
"Garud Iyengar",
"Assaf Zeevi"
] |
We consider Pareto front identification for linear bandits (PFILin) where the
goal is to identify a set of arms whose reward vectors are not dominated by any
of the others when the mean reward vector is a linear function of the context.
PFILin includes the best arm identification problem and multi-objective active
learning as special cases. The sample complexity of our proposed algorithm is
$\tilde{O}(d/\Delta^2)$, where $d$ is the dimension of contexts and $\Delta$ is
a measure of problem complexity. Our sample complexity is optimal up to a
logarithmic factor. A novel feature of our algorithm is that it uses the
contexts of all actions. In addition to efficiently identifying the Pareto
front, our algorithm also guarantees $\tilde{O}(\sqrt{d/t})$ bound for
instantaneous Pareto regret when the number of samples is larger than
$\Omega(d\log dL)$ for $L$ dimensional vector rewards. By using the contexts of
all arms, our proposed algorithm simultaneously provides efficient Pareto front
identification and regret minimization. Numerical experiments demonstrate that
the proposed algorithm successfully identifies the Pareto front while
minimizing the regret.
|
[
"stat.ML",
"cs.LG"
] | false |
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