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|---|---|---|---|---|---|---|
2305.13711
|
2023-05-23T05:57:09Z
|
LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain
Conversations with Large Language Models
|
[
"Yen-Ting Lin",
"Yun-Nung Chen"
] |
We propose LLM-Eval, a unified multi-dimensional automatic evaluation method
for open-domain conversations with large language models (LLMs). Existing
evaluation methods often rely on human annotations, ground-truth responses, or
multiple LLM prompts, which can be expensive and time-consuming. To address
these issues, we design a single prompt-based evaluation method that leverages
a unified evaluation schema to cover multiple dimensions of conversation
quality in a single model call. We extensively evaluate the performance of
LLM-Eval on various benchmark datasets, demonstrating its effectiveness,
efficiency, and adaptability compared to state-of-the-art evaluation methods.
Our analysis also highlights the importance of choosing suitable LLMs and
decoding strategies for accurate evaluation results. LLM-Eval offers a
versatile and robust solution for evaluating open-domain conversation systems,
streamlining the evaluation process and providing consistent performance across
diverse scenarios.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13712
|
2023-05-23T05:59:21Z
|
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large
Language Models
|
[
"Alfonso Amayuelas",
"Liangming Pan",
"Wenhu Chen",
"William Wang"
] |
This paper investigates the capabilities of Large Language Models (LLMs) in
the context of understanding their own knowledge and measuring their
uncertainty. We argue this is an important feature for mitigating
hallucinations. Specifically, we focus on addressing \textit{known-unknown}
questions, characterized by high uncertainty due to the absence of definitive
answers. To facilitate our study, we collect a dataset with new Known-Unknown
Questions (KUQ) and propose a novel categorization scheme to elucidate the
sources of uncertainty. Subsequently, we assess the LLMs' ability to
differentiate between known and unknown questions and classify them
accordingly. Moreover, we evaluate the quality of their answers in an
Open-Ended QA setting. To quantify the uncertainty expressed in the answers, we
create a semantic evaluation method that measures the model's accuracy in
expressing uncertainty between known vs unknown questions.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13725
|
2023-05-23T06:21:31Z
|
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
|
[
"Raghav Gupta",
"Renat Aksitov",
"Samrat Phatale",
"Simral Chaudhary",
"Harrison Lee",
"Abhinav Rastogi"
] |
Conversational recommendation systems (CRS) aim to recommend suitable items
to users through natural language conversation. However, most CRS approaches do
not effectively utilize the signal provided by these conversations. They rely
heavily on explicit external knowledge e.g., knowledge graphs to augment the
models' understanding of the items and attributes, which is quite hard to
scale. To alleviate this, we propose an alternative information retrieval
(IR)-styled approach to the CRS item recommendation task, where we represent
conversations as queries and items as documents to be retrieved. We expand the
document representation used for retrieval with conversations from the training
set. With a simple BM25-based retriever, we show that our task formulation
compares favorably with much more complex baselines using complex external
knowledge on a popular CRS benchmark. We demonstrate further improvements using
user-centric modeling and data augmentation to counter the cold start problem
for CRSs.
|
[
"cs.CL",
"cs.IR"
] | false |
2305.13755
|
2023-05-23T07:13:51Z
|
Topic-driven Distant Supervision Framework for Macro-level Discourse
Parsing
|
[
"Feng Jiang",
"Longwang He",
"Peifeng Li",
"Qiaoming Zhu",
"Haizhou Li"
] |
Discourse parsing, the task of analyzing the internal rhetorical structure of
texts, is a challenging problem in natural language processing. Despite the
recent advances in neural models, the lack of large-scale, high-quality corpora
for training remains a major obstacle. Recent studies have attempted to
overcome this limitation by using distant supervision, which utilizes results
from other NLP tasks (e.g., sentiment polarity, attention matrix, and
segmentation probability) to parse discourse trees. However, these methods do
not take into account the differences between in-domain and out-of-domain
tasks, resulting in lower performance and inability to leverage the
high-quality in-domain data for further improvement. To address these issues,
we propose a distant supervision framework that leverages the relations between
topic structure and rhetorical structure. Specifically, we propose two
distantly supervised methods, based on transfer learning and the
teacher-student model, that narrow the gap between in-domain and out-of-domain
tasks through label mapping and oracle annotation. Experimental results on the
MCDTB and RST-DT datasets show that our methods achieve the best performance in
both distant-supervised and supervised scenarios.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13775
|
2023-05-23T07:44:52Z
|
Concept-aware Training Improves In-context Learning Ability of Language
Models
|
[
"Michal Štefánik",
"Marek Kadlčík"
] |
Many recent language models (LMs) of Transformers family exhibit so-called
in-context learning (ICL) ability, manifested in the LMs' ability to modulate
their function by a task described in a natural language input. Previous work
curating these models assumes that ICL emerges from vast over-parametrization
or the scale of multi-task training. However, a complementary branch of recent
theoretical work attributes ICL emergence to specific properties of training
data and creates functional in-context learners in small-scale, synthetic
settings.
Inspired by recent findings on data properties driving the emergence of ICL,
we propose a method to create LMs able to better utilize the in-context
information, by constructing training scenarios where it is beneficial for the
LM to capture the analogical reasoning concepts. We measure that data sampling
of Concept-aware Training (CoAT) consistently improves models' reasoning
ability. As a result, the in-context learners trained with CoAT on only two
datasets of a single (QA) task perform comparably to larger models trained on
1600+ tasks.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13776
|
2023-05-23T07:45:17Z
|
Counterspeeches up my sleeve! Intent Distribution Learning and
Persistent Fusion for Intent-Conditioned Counterspeech Generation
|
[
"Rishabh Gupta",
"Shaily Desai",
"Manvi Goel",
"Anil Bandhakavi",
"Tanmoy Chakraborty",
"Md. Shad Akhtar"
] |
Counterspeech has been demonstrated to be an efficacious approach for
combating hate speech. While various conventional and controlled approaches
have been studied in recent years to generate counterspeech, a counterspeech
with a certain intent may not be sufficient in every scenario. Due to the
complex and multifaceted nature of hate speech, utilizing multiple forms of
counter-narratives with varying intents may be advantageous in different
circumstances. In this paper, we explore intent-conditioned counterspeech
generation. At first, we develop IntentCONAN, a diversified intent-specific
counterspeech dataset with 6831 counterspeeches conditioned on five intents,
i.e., informative, denouncing, question, positive, and humour. Subsequently, we
propose QUARC, a two-stage framework for intent-conditioned counterspeech
generation. QUARC leverages vector-quantized representations learned for each
intent category along with PerFuMe, a novel fusion module to incorporate
intent-specific information into the model. Our evaluation demonstrates that
QUARC outperforms several baselines by an average of 10% across evaluation
metrics. An extensive human evaluation supplements our hypothesis of better and
more appropriate responses than comparative systems.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13794
|
2023-05-23T08:05:43Z
|
Personalized Predictive ASR for Latency Reduction in Voice Assistants
|
[
"Andreas Schwarz",
"Di He",
"Maarten Van Segbroeck",
"Mohammed Hethnawi",
"Ariya Rastrow"
] |
Streaming Automatic Speech Recognition (ASR) in voice assistants can utilize
prefetching to partially hide the latency of response generation. Prefetching
involves passing a preliminary ASR hypothesis to downstream systems in order to
prefetch and cache a response. If the final ASR hypothesis after endpoint
detection matches the preliminary one, the cached response can be delivered to
the user, thus saving latency. In this paper, we extend this idea by
introducing predictive automatic speech recognition, where we predict the full
utterance from a partially observed utterance, and prefetch the response based
on the predicted utterance. We introduce two personalization approaches and
investigate the tradeoff between potential latency gains from successful
predictions and the cost increase from failed predictions. We evaluate our
methods on an internal voice assistant dataset as well as the public SLURP
dataset.
|
[
"cs.CL",
"eess.AS"
] | false |
2305.13917
|
2023-05-23T10:44:00Z
|
Generating Data for Symbolic Language with Large Language Models
|
[
"Jiacheng Ye",
"Chengzu Li",
"Lingpeng Kong",
"Tao Yu"
] |
While large language models (LLMs) bring not only performance but also
complexity, recent work has started to turn LLMs into data generators rather
than task inferencers, where another affordable task model is trained for
efficient deployment and inference. However, such an approach has primarily
been applied to natural language tasks and has not yet been explored for
symbolic language tasks with complex structured outputs (e.g., semantic parsing
and code generation). In this paper, we propose SymGen which utilizes LLMs for
generating various annotation-expensive symbolic language data. SymGen consists
of an informative prompt to steer generation and an agreement-based verifier to
improve data correctness. We conduct extensive experiments on six symbolic
language tasks across various settings. Compared with the LLMs, we demonstrate
the 1\%-sized task model can achieve comparable or better performance, largely
cutting inference and deployment costs. We also show that generated data with
only a few human demonstrations can be as effective as over 10 times the amount
of human-annotated data when training the task model, saving a considerable
amount of annotation effort. SymGen sheds new light on data generation for
complex tasks, and we release the code at
\href{https://github.com/HKUNLP/SymGen}{https://github.com/HKUNLP/SymGen}.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14087
|
2023-05-23T14:11:42Z
|
BM25 Query Augmentation Learned End-to-End
|
[
"Xiaoyin Chen",
"Sam Wiseman"
] |
Given BM25's enduring competitiveness as an information retrieval baseline,
we investigate to what extent it can be even further improved by augmenting and
re-weighting its sparse query-vector representation. We propose an approach to
learning an augmentation and a re-weighting end-to-end, and we find that our
approach improves performance over BM25 while retaining its speed. We
furthermore find that the learned augmentations and re-weightings transfer well
to unseen datasets.
|
[
"cs.CL",
"cs.IR"
] | false |
2305.14104
|
2023-05-23T14:26:11Z
|
Out-of-Distribution Generalization in Text Classification: Past,
Present, and Future
|
[
"Linyi Yang",
"Yaoxiao Song",
"Xuan Ren",
"Chenyang Lyu",
"Yidong Wang",
"Lingqiao Liu",
"Jindong Wang",
"Jennifer Foster",
"Yue Zhang"
] |
Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where
the test distribution differs from the training data distribution. This poses
important questions about the robustness of NLP models and their high accuracy,
which may be artificially inflated due to their underlying sensitivity to
systematic biases. Despite these challenges, there is a lack of comprehensive
surveys on the generalization challenge from an OOD perspective in text
classification. Therefore, this paper aims to fill this gap by presenting the
first comprehensive review of recent progress, methods, and evaluations on this
topic. We furth discuss the challenges involved and potential future research
directions. By providing quick access to existing work, we hope this survey
will encourage future research in this area.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14126
|
2023-05-23T14:53:20Z
|
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge
Graph Completion
|
[
"Rui Li",
"Xu Chen",
"Chaozhuo Li",
"Yanming Shen",
"Jianan Zhao",
"Yujing Wang",
"Weihao Han",
"Hao Sun",
"Weiwei Deng",
"Qi Zhang",
"Xing Xie"
] |
Embedding models have shown great power in knowledge graph completion (KGC)
task. By learning structural constraints for each training triple, these
methods implicitly memorize intrinsic relation rules to infer missing links.
However, this paper points out that the multi-hop relation rules are hard to be
reliably memorized due to the inherent deficiencies of such implicit
memorization strategy, making embedding models underperform in predicting links
between distant entity pairs. To alleviate this problem, we present Vertical
Learning Paradigm (VLP), which extends embedding models by allowing to
explicitly copy target information from related factual triples for more
accurate prediction. Rather than solely relying on the implicit memory, VLP
directly provides additional cues to improve the generalization ability of
embedding models, especially making the distant link prediction significantly
easier. Moreover, we also propose a novel relative distance based negative
sampling technique (ReD) for more effective optimization. Experiments
demonstrate the validity and generality of our proposals on two standard
benchmarks. Our code is available at https://github.com/rui9812/VLP.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14128
|
2023-05-23T14:55:25Z
|
Dr.ICL: Demonstration-Retrieved In-context Learning
|
[
"Man Luo",
"Xin Xu",
"Zhuyun Dai",
"Panupong Pasupat",
"Mehran Kazemi",
"Chitta Baral",
"Vaiva Imbrasaite",
"Vincent Y Zhao"
] |
In-context learning (ICL), teaching a large language model (LLM) to perform a
task with few-shot demonstrations rather than adjusting the model parameters,
has emerged as a strong paradigm for using LLMs. While early studies primarily
used a fixed or random set of demonstrations for all test queries, recent
research suggests that retrieving semantically similar demonstrations to the
input from a pool of available demonstrations results in better performance.
This work expands the applicability of retrieval-based ICL approaches by
demonstrating that even simple word-overlap similarity measures such as BM25
outperform randomly selected demonstrations. Furthermore, we extend the success
of retrieval-based ICL to instruction-finetuned LLMs as well as
Chain-of-Thought (CoT) prompting. For instruction-finetuned LLMs, we find that
although a model has already seen the training data at training time,
retrieving demonstrations from the training data at test time yields better
results compared to using no demonstrations or random demonstrations. Last but
not least, we train a task-specific demonstration retriever that outperforms
off-the-shelf retrievers.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14150
|
2023-05-23T15:15:11Z
|
WYWEB: A NLP Evaluation Benchmark For Classical Chinese
|
[
"Bo Zhou",
"Qianglong Chen",
"Tianyu Wang",
"Xiaomi Zhong",
"Yin Zhang"
] |
To fully evaluate the overall performance of different NLP models in a given
domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and
CLUE. The fi eld of natural language understanding has traditionally focused on
benchmarks for various tasks in languages such as Chinese, English, and
multilingua, however, there has been a lack of attention given to the area of
classical Chinese, also known as "wen yan wen", which has a rich history
spanning thousands of years and holds signifi cant cultural and academic value.
For the prosperity of the NLP community, in this paper, we introduce the WYWEB
evaluation benchmark, which consists of nine NLP tasks in classical Chinese,
implementing sentence classifi cation, sequence labeling, reading
comprehension, and machine translation. We evaluate the existing pre-trained
language models, which are all struggling with this benchmark. We also
introduce a number of supplementary datasets and additional tools to help
facilitate further progress on classical Chinese NLU. The github repository is
https://github.com/baudzhou/WYWEB.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14169
|
2023-05-23T15:38:37Z
|
EASE: An Easily-Customized Annotation System Powered by Efficiency
Enhancement Mechanisms
|
[
"Naihao Deng",
"Yikai Liu",
"Mingye Chen",
"Winston Wu",
"Siyang Liu",
"Yulong Chen",
"Yue Zhang",
"Rada Mihalcea"
] |
The performance of current supervised AI systems is tightly connected to the
availability of annotated datasets. Annotations are usually collected through
annotation tools, which are often designed for specific tasks and are difficult
to customize. Moreover, existing annotation tools with an active learning
mechanism often only support limited use cases. To address these limitations,
we present EASE, an Easily-Customized Annotation System Powered by Efficiency
Enhancement Mechanisms. \sysname provides modular annotation units for building
customized annotation interfaces and also provides multiple back-end options
that suggest annotations using (1) multi-task active learning; (2) demographic
feature based active learning; (3) a prompt system that can query the API of
large language models. We conduct multiple experiments and user studies to
evaluate our system's flexibility and effectiveness. Our results show that our
system can meet the diverse needs of NLP researchers and significantly
accelerate the annotation process.
|
[
"cs.HC",
"cs.CL"
] | false |
2305.14200
|
2023-05-23T16:19:30Z
|
Accessing Higher Dimensions for Unsupervised Word Translation
|
[
"Sida I. Wang"
] |
The striking ability of unsupervised word translation has been demonstrated
with the help of word vectors / pretraining; however, they require large
amounts of data and usually fails if the data come from different domains. We
propose coocmap, a method that can use either high-dimensional co-occurrence
counts or their lower-dimensional approximations. Freed from the limits of low
dimensions, we show that relying on low-dimensional vectors and their
incidental properties miss out on better denoising methods and useful world
knowledge in high dimensions, thus stunting the potential of the data. Our
results show that unsupervised translation can be achieved more easily and
robustly than previously thought -- less than 80MB and minutes of CPU time is
required to achieve over 50\% accuracy for English to Finnish, Hungarian, and
Chinese translations when trained on similar data; even under domain mismatch,
we show coocmap still works fully unsupervised on English NewsCrawl to Chinese
Wikipedia and English Europarl to Spanish Wikipedia, among others. These
results challenge prevailing assumptions on the necessity and superiority of
low-dimensional vectors, and suggest that similarly processed co-occurrences
can outperform dense vectors on other tasks too.
|
[
"cs.CL",
"cs.LG",
"I.2.7"
] | false |
2305.14233
|
2023-05-23T16:49:14Z
|
Enhancing Chat Language Models by Scaling High-quality Instructional
Conversations
|
[
"Ning Ding",
"Yulin Chen",
"Bokai Xu",
"Yujia Qin",
"Zhi Zheng",
"Shengding Hu",
"Zhiyuan Liu",
"Maosong Sun",
"Bowen Zhou"
] |
Fine-tuning on instruction data has been widely validated as an effective
practice for implementing chat language models like ChatGPT. Scaling the
diversity and quality of such data, although straightforward, stands a great
chance of leading to improved performance. This paper aims to improve the upper
bound of open-source models further. We first provide a systematically
designed, diverse, informative, large-scale dataset of instructional
conversations, UltraChat, which does not involve human queries. Our objective
is to capture the breadth of interactions that a human might have with an AI
assistant and employs a comprehensive framework to generate multi-turn
conversation iteratively. UltraChat contains 1.5 million high-quality
multi-turn dialogues and covers a wide range of topics and instructions. Our
statistical analysis of UltraChat reveals its superiority in various key
metrics, including scale, average length, diversity, coherence, etc.,
solidifying its position as a leading open-source dataset. Building upon
UltraChat, we fine-tune a LLaMA model to create a powerful conversational
model, UltraLLaMA. Our evaluations indicate that UltraLLaMA consistently
outperforms other open-source models, including Vicuna, the previously
recognized state-of-the-art open-source model. The dataset and the model will
be publicly released\footnote{\url{https://github.com/thunlp/UltraChat}}.
|
[
"cs.CL",
"cs.AI"
] | true |
2305.14237
|
2023-05-23T16:53:49Z
|
HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale
Supervision
|
[
"Wenting Zhao",
"Justin T. Chiu",
"Claire Cardie",
"Alexander M. Rush"
] |
Explainable multi-hop question answering (QA) not only predicts answers but
also identifies rationales, i. e. subsets of input sentences used to derive the
answers. This problem has been extensively studied under the supervised
setting, where both answer and rationale annotations are given. Because
rationale annotations are expensive to collect and not always available, recent
efforts have been devoted to developing methods that do not rely on supervision
for rationales. However, such methods have limited capacities in modeling
interactions between sentences, let alone reasoning across multiple documents.
This work proposes a principled, probabilistic approach for training
explainable multi-hop QA systems without rationale supervision. Our approach
performs multi-hop reasoning by explicitly modeling rationales as sets,
enabling the model to capture interactions between documents and sentences
within a document. Experimental results show that our approach is more accurate
at selecting rationales than the previous methods, while maintaining similar
accuracy in predicting answers.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14299
|
2023-05-23T17:40:41Z
|
TaDSE: Template-aware Dialogue Sentence Embeddings
|
[
"Minsik Oh",
"Jiwei Li",
"Guoyin Wang"
] |
Learning high quality sentence embeddings from dialogues has drawn increasing
attentions as it is essential to solve a variety of dialogue-oriented tasks
with low annotation cost. However, directly annotating and gathering utterance
relationships in conversations are difficult, while token-level annotations,
\eg, entities, slots and templates, are much easier to obtain. General sentence
embedding methods are usually sentence-level self-supervised frameworks and
cannot utilize token-level extra knowledge. In this paper, we introduce
Template-aware Dialogue Sentence Embedding (TaDSE), a novel augmentation method
that utilizes template information to effectively learn utterance
representation via self-supervised contrastive learning framework. TaDSE
augments each sentence with its corresponding template and then conducts
pairwise contrastive learning over both sentence and template. We further
enhance the effect with a synthetically augmented dataset that enhances
utterance-template relation, in which entity detection (slot-filling) is a
preliminary step. We evaluate TaDSE performance on five downstream benchmark
datasets. The experiment results show that TaDSE achieves significant
improvements over previous SOTA methods, along with a consistent Intent
Classification task performance improvement margin. We further introduce a
novel analytic instrument of Semantic Compression method, for which we discover
a correlation with uniformity and alignment. Our code will be released soon.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14307
|
2023-05-23T17:45:54Z
|
Debiasing should be Good and Bad: Measuring the Consistency of Debiasing
Techniques in Language Models
|
[
"Robert Morabito",
"Jad Kabbara",
"Ali Emami"
] |
Debiasing methods that seek to mitigate the tendency of Language Models (LMs)
to occasionally output toxic or inappropriate text have recently gained
traction. In this paper, we propose a standardized protocol which distinguishes
methods that yield not only desirable results, but are also consistent with
their mechanisms and specifications. For example, we ask, given a debiasing
method that is developed to reduce toxicity in LMs, if the definition of
toxicity used by the debiasing method is reversed, would the debiasing results
also be reversed? We used such considerations to devise three criteria for our
new protocol: Specification Polarity, Specification Importance, and Domain
Transferability. As a case study, we apply our protocol to a popular debiasing
method, Self-Debiasing, and compare it to one we propose, called Instructive
Debiasing, and demonstrate that consistency is as important an aspect to
debiasing viability as is simply a desirable result. We show that our protocol
provides essential insights into the generalizability and interpretability of
debiasing methods that may otherwise go overlooked.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14483
|
2023-05-23T19:25:52Z
|
Language Model Self-improvement by Reinforcement Learning Contemplation
|
[
"Jing-Cheng Pang",
"Pengyuan Wang",
"Kaiyuan Li",
"Xiong-Hui Chen",
"Jiacheng Xu",
"Zongzhang Zhang",
"Yang Yu"
] |
Large Language Models (LLMs) have exhibited remarkable performance across
various natural language processing (NLP) tasks. However, fine-tuning these
models often necessitates substantial supervision, which can be expensive and
time-consuming to obtain. This paper introduces a novel unsupervised method
called LanguageModel Self-Improvement by Reinforcement Learning Contemplation
(SIRLC) that improves LLMs without reliance on external labels. Our approach is
grounded in the observation that it is simpler for language models to assess
text quality than to generate text. Building on this insight, SIRLC assigns
LLMs dual roles as both student and teacher. As a student, the LLM generates
answers to unlabeled questions, while as a teacher, it evaluates the generated
text and assigns scores accordingly. The model parameters are updated using
reinforcement learning to maximize the evaluation score. We demonstrate that
SIRLC can be applied to various NLP tasks, such as reasoning problems, text
generation, and machine translation. Our experiments show that SIRLC
effectively improves LLM performance without external supervision, resulting in
a 5.6% increase in answering accuracy for reasoning tasks and a rise in
BERTScore from 0.82 to 0.86 for translation tasks. Furthermore, SIRLC can be
applied to models of different sizes, showcasing its broad applicability.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.14538
|
2023-05-23T21:48:02Z
|
Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural
Machine Translation
|
[
"Frédéric Odermatt",
"Béni Egressy",
"Roger Wattenhofer"
] |
This paper presents a plug-and-play approach for translation with terminology
constraints. Terminology constraints are an important aspect of many modern
translation pipelines. In both specialized domains and newly emerging domains
(such as the COVID-19 pandemic), accurate translation of technical terms is
crucial. Recent approaches often train models to copy terminologies from the
input into the output sentence by feeding the target terminology along with the
input. But this requires expensive training whenever the underlying language
model is changed or the system should specialize to a new domain. We propose
Cascade Beam Search, a plug-and-play terminology-forcing approach that requires
no training. Cascade Beam Search has two parts: 1) logit manipulation to
increase the probability of target terminologies and 2) a cascading beam setup
based on grid beam search, where beams are grouped by the number of
terminologies they contain. We evaluate the performance of our approach by
competing against the top submissions of the WMT21 terminology translation
task. Our plug-and-play approach performs on par with the winning submissions
without using a domain-specific language model and with no additional training.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14556
|
2023-05-23T22:31:01Z
|
Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented
Dialogues and Annotations
|
[
"Tiziano Labruna",
"Sofia Brenna",
"Andrea Zaninello",
"Bernardo Magnini"
] |
Large pre-trained language models have exhibited unprecedented capabilities
in producing high-quality text via prompting techniques. This fact introduces
new possibilities for data collection and annotation, particularly in
situations where such data is scarce, complex to gather, expensive, or even
sensitive. In this paper, we explore the potential of these models to generate
and annotate goal-oriented dialogues, and conduct an in-depth analysis to
evaluate their quality. Our experiments employ ChatGPT, and encompass three
categories of goal-oriented dialogues (task-oriented, collaborative, and
explanatory), two generation modes (interactive and one-shot), and two
languages (English and Italian). Based on extensive human-based evaluations, we
demonstrate that the quality of generated dialogues and annotations is on par
with those generated by humans.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.14574
|
2023-05-23T23:23:49Z
|
Detecting and Mitigating Indirect Stereotypes in Word Embeddings
|
[
"Erin George",
"Joyce Chew",
"Deanna Needell"
] |
Societal biases in the usage of words, including harmful stereotypes, are
frequently learned by common word embedding methods. These biases manifest not
only between a word and an explicit marker of its stereotype, but also between
words that share related stereotypes. This latter phenomenon, sometimes called
"indirect bias,'' has resisted prior attempts at debiasing. In this paper, we
propose a novel method called Biased Indirect Relationship Modification (BIRM)
to mitigate indirect bias in distributional word embeddings by modifying biased
relationships between words before embeddings are learned. This is done by
considering how the co-occurrence probability of a given pair of words changes
in the presence of words marking an attribute of bias, and using this to
average out the effect of a bias attribute. To evaluate this method, we perform
a series of common tests and demonstrate that measures of bias in the word
embeddings are reduced in exchange for minor reduction in the semantic quality
of the embeddings. In addition, we conduct novel tests for measuring indirect
stereotypes by extending the Word Embedding Association Test (WEAT) with new
test sets for indirect binary gender stereotypes. With these tests, we
demonstrate the presence of more subtle stereotypes not addressed by previous
work. The proposed method is able to reduce the presence of some of these new
stereotypes, serving as a crucial next step towards non-stereotyped word
embeddings.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.14587
|
2023-05-23T23:53:29Z
|
Contextualized Topic Coherence Metrics
|
[
"Hamed Rahimi",
"Jacob Louis Hoover",
"David Mimno",
"Hubert Naacke",
"Camelia Constantin",
"Bernd Amann"
] |
The recent explosion in work on neural topic modeling has been criticized for
optimizing automated topic evaluation metrics at the expense of actual
meaningful topic identification. But human annotation remains expensive and
time-consuming. We propose LLM-based methods inspired by standard human topic
evaluations, in a family of metrics called Contextualized Topic Coherence
(CTC). We evaluate both a fully automated version as well as a semi-automated
CTC that allows human-centered evaluation of coherence while maintaining the
efficiency of automated methods. We evaluate CTC relative to five other metrics
on six topic models and find that it outperforms automated topic coherence
methods, works well on short documents, and is not susceptible to meaningless
but high-scoring topics.
|
[
"cs.CL",
"cs.IR"
] | false |
2305.14588
|
2023-05-23T23:53:58Z
|
Evaluating end-to-end entity linking on domain-specific knowledge bases:
Learning about ancient technologies from museum collections
|
[
"Sebastian Cadavid-Sanchez",
"Khalil Kacem",
"Rafael Aparecido Martins Frade",
"Johannes Boehm",
"Thomas Chaney",
"Danial Lashkari",
"Daniel Simig"
] |
To study social, economic, and historical questions, researchers in the
social sciences and humanities have started to use increasingly large
unstructured textual datasets. While recent advances in NLP provide many tools
to efficiently process such data, most existing approaches rely on generic
solutions whose performance and suitability for domain-specific tasks is not
well understood. This work presents an attempt to bridge this domain gap by
exploring the use of modern Entity Linking approaches for the enrichment of
museum collection data. We collect a dataset comprising of more than 1700 texts
annotated with 7,510 mention-entity pairs, evaluate some off-the-shelf
solutions in detail using this dataset and finally fine-tune a recent
end-to-end EL model on this data. We show that our fine-tuned model
significantly outperforms other approaches currently available in this domain
and present a proof-of-concept use case of this model. We release our dataset
and our best model.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.16334
|
2023-05-23T09:36:51Z
|
OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
|
[
"Yuanzhen Xie",
"Tao Xie",
"Mingxiong Lin",
"WenTao Wei",
"Chenglin Li",
"Beibei Kong",
"Lei Chen",
"Chengxiang Zhuo",
"Bo Hu",
"Zang Li"
] |
In most current research, large language models (LLMs) are able to perform
reasoning tasks by generating chains of thought through the guidance of
specific prompts. However, there still exists a significant discrepancy between
their capability in solving complex reasoning problems and that of humans. At
present, most approaches focus on chains of thought (COT) and tool use, without
considering the adoption and application of human cognitive frameworks. It is
well-known that when confronting complex reasoning challenges, humans typically
employ various cognitive abilities, and necessitate interaction with all
aspects of tools, knowledge, and the external environment information to
accomplish intricate tasks. This paper introduces a novel intelligent
framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive
architecture framework, and propose to simulate certain aspects of human
cognition. The framework involves approximating different cognitive modules,
including attention, memory, reasoning, learning, and corresponding scheduling
and decision-making mechanisms. Inspired by the active learning mechanism of
human beings, it proposes a learning unit to record previous mistakes and
expert opinions, and dynamically refer to them to strengthen their ability to
solve similar problems. The paper also outlines common effective reasoning
frameworks for human problem-solving and designs Chain-of-Thought (COT)
templates accordingly. A comprehensive decision-making mechanism is also
proposed to maximize model accuracy. The efficacy of OlaGPT has been
stringently evaluated on multiple reasoning datasets, and the experimental
outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks,
demonstrating its superior performance. Our implementation of OlaGPT is
available on GitHub: \url{https://github.com/oladata-team/OlaGPT}.
|
[
"cs.CL",
"cs.AI"
] | true |
2305.18323
|
2023-05-23T00:16:48Z
|
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented
Language Models
|
[
"Binfeng Xu",
"Zhiyuan Peng",
"Bowen Lei",
"Subhabrata Mukherjee",
"Yuchen Liu",
"Dongkuan Xu"
] |
Augmented Language Models (ALMs) blend the reasoning capabilities of Large
Language Models (LLMs) with tools that allow for knowledge retrieval and action
execution. Existing ALM systems trigger LLM thought processes while pulling
observations from these tools in an interleaved fashion. Specifically, an LLM
reasons to call an external tool, gets halted to fetch the tool's response, and
then decides the next action based on all preceding response tokens. Such a
paradigm, though straightforward and easy to implement, often leads to huge
computation complexity from redundant prompts and repeated execution. This
study addresses such challenges for the first time, proposing a modular
paradigm ReWOO (Reasoning WithOut Observation) that detaches the reasoning
process from external observations, thus significantly reducing token
consumption. Comprehensive evaluations across six public NLP benchmarks and a
curated dataset reveal consistent performance enhancements with our proposed
methodology. Notably, ReWOO achieves 5x token efficiency and 4% accuracy
improvement on HotpotQA, a multi-step reasoning benchmark. Furthermore, ReWOO
demonstrates robustness under tool-failure scenarios. Beyond prompt efficiency,
decoupling parametric modules from non-parametric tool calls enables
instruction fine-tuning to offload LLMs into smaller language models, thus
substantially reducing model parameters. Our illustrative work offloads
reasoning ability from 175B GPT3.5 into 7B LLaMA, demonstrating the significant
potential for truly efficient and scalable ALM systems.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.18324
|
2023-05-23T03:26:32Z
|
Regex-augmented Domain Transfer Topic Classification based on a
Pre-trained Language Model: An application in Financial Domain
|
[
"Vanessa Liao",
"Syed Shariyar Murtaza",
"Yifan Nie",
"Jimmy Lin"
] |
A common way to use large pre-trained language models for downstream tasks is
to fine tune them using additional layers. This may not work well if downstream
domain is a specialized domain whereas the large language model has been
pre-trained on a generic corpus. In this paper, we discuss the use of regular
expression patterns employed as features for domain knowledge during the
process of fine tuning, in addition to domain specific text. Our experiments on
real scenario production data show that this method of fine tuning improves the
downstream text classification tasks as compared to fine tuning only on domain
specific text. We also show that the use of attention network for fine tuning
improves results compared to simple linear layers.
|
[
"cs.CL",
"cs.AI"
] | false |
2306.05540
|
2023-05-23T11:18:30Z
|
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of
Machine-Generated Text
|
[
"Jinyan Su",
"Terry Yue Zhuo",
"Di Wang",
"Preslav Nakov"
] |
With the rapid progress of large language models (LLMs) and the huge amount
of text they generated, it becomes more and more impractical to manually
distinguish whether a text is machine-generated. Given the growing use of LLMs
in social media and education, it prompts us to develop methods to detect
machine-generated text, preventing malicious usage such as plagiarism,
misinformation, and propaganda. Previous work has studied several zero-shot
methods, which require no training data. These methods achieve good
performance, but there is still a lot of room for improvement. In this paper,
we introduce two novel zero-shot methods for detecting machine-generated text
by leveraging the log rank information. One is called DetectLLM-LRR, which is
fast and efficient, and the other is called DetectLLM-NPR, which is more
accurate, but slower due to the need for perturbations. Our experiments on
three datasets and seven language models show that our proposed methods improve
over the state of the art by 3.9 and 1.75 AUROC points absolute. Moreover,
DetectLLM-NPR needs fewer perturbations than previous work to achieve the same
level of performance, which makes it more practical for real-world use. We also
investigate the efficiency--performance trade-off based on users preference on
these two measures and we provide intuition for using them in practice
effectively. We release the data and the code of both methods in
https://github.com/mbzuai-nlp/DetectLLM
|
[
"cs.CL",
"cs.AI",
"68T50",
"F.2.2; I.2.7"
] | false |
2305.13713
|
2023-05-23T06:04:50Z
|
CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling
and Attentive Listening in Customer Center
|
[
"Yuki Saito",
"Eiji Iimori",
"Shinnosuke Takamichi",
"Kentaro Tachibana",
"Hiroshi Saruwatari"
] |
We present CALLS, a Japanese speech corpus that considers phone calls in a
customer center as a new domain of empathetic spoken dialogue. The existing
STUDIES corpus covers only empathetic dialogue between a teacher and student in
a school. To extend the application range of empathetic dialogue speech
synthesis (EDSS), we designed our corpus to include the same female speaker as
the STUDIES teacher, acting as an operator in simulated phone calls. We
describe a corpus construction methodology and analyze the recorded speech. We
also conduct EDSS experiments using the CALLS and STUDIES corpora to
investigate the effect of domain differences. The results show that mixing the
two corpora during training causes biased improvements in the quality of
synthetic speech due to the different degrees of expressiveness. Our project
page of the corpus is http://sython.org/Corpus/STUDIES-2.
|
[
"cs.SD",
"cs.CL",
"cs.LG",
"eess.AS"
] | false |
2305.13724
|
2023-05-23T06:19:37Z
|
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from
ChatGPT-derived Context Word Embeddings
|
[
"Yuki Saito",
"Shinnosuke Takamichi",
"Eiji Iimori",
"Kentaro Tachibana",
"Hiroshi Saruwatari"
] |
We propose ChatGPT-EDSS, an empathetic dialogue speech synthesis (EDSS)
method using ChatGPT for extracting dialogue context. ChatGPT is a chatbot that
can deeply understand the content and purpose of an input prompt and
appropriately respond to the user's request. We focus on ChatGPT's reading
comprehension and introduce it to EDSS, a task of synthesizing speech that can
empathize with the interlocutor's emotion. Our method first gives chat history
to ChatGPT and asks it to generate three words representing the intention,
emotion, and speaking style for each line in the chat. Then, it trains an EDSS
model using the embeddings of ChatGPT-derived context words as the conditioning
features. The experimental results demonstrate that our method performs
comparably to ones using emotion labels or neural network-derived context
embeddings learned from chat histories. The collected ChatGPT-derived context
information is available at
https://sarulab-speech.github.io/demo_ChatGPT_EDSS/.
|
[
"cs.SD",
"cs.CL",
"cs.LG",
"eess.AS"
] | false |
2305.13729
|
2023-05-23T06:35:33Z
|
Discrete Prompt Optimization via Constrained Generation for Zero-shot
Re-ranker
|
[
"Sukmin Cho",
"Soyeong Jeong",
"Jeongyeon Seo",
"Jong C. Park"
] |
Re-rankers, which order retrieved documents with respect to the relevance
score on the given query, have gained attention for the information retrieval
(IR) task. Rather than fine-tuning the pre-trained language model (PLM), the
large-scale language model (LLM) is utilized as a zero-shot re-ranker with
excellent results. While LLM is highly dependent on the prompts, the impact and
the optimization of the prompts for the zero-shot re-ranker are not explored
yet. Along with highlighting the impact of optimization on the zero-shot
re-ranker, we propose a novel discrete prompt optimization method, Constrained
Prompt generation (Co-Prompt), with the metric estimating the optimum for
re-ranking. Co-Prompt guides the generated texts from PLM toward optimal
prompts based on the metric without parameter update. The experimental results
demonstrate that Co-Prompt leads to outstanding re-ranking performance against
the baselines. Also, Co-Prompt generates more interpretable prompts for humans
against other prompt optimization methods.
|
[
"cs.IR",
"cs.AI",
"cs.CL"
] | false |
2305.13831
|
2023-05-23T08:52:00Z
|
ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech
Synthesis with Diffusion and Style-based Models
|
[
"Minki Kang",
"Wooseok Han",
"Sung Ju Hwang",
"Eunho Yang"
] |
Emotional Text-To-Speech (TTS) is an important task in the development of
systems (e.g., human-like dialogue agents) that require natural and emotional
speech. Existing approaches, however, only aim to produce emotional TTS for
seen speakers during training, without consideration of the generalization to
unseen speakers. In this paper, we propose ZET-Speech, a zero-shot adaptive
emotion-controllable TTS model that allows users to synthesize any speaker's
emotional speech using only a short, neutral speech segment and the target
emotion label. Specifically, to enable a zero-shot adaptive TTS model to
synthesize emotional speech, we propose domain adversarial learning and
guidance methods on the diffusion model. Experimental results demonstrate that
ZET-Speech successfully synthesizes natural and emotional speech with the
desired emotion for both seen and unseen speakers. Samples are at
https://ZET-Speech.github.io/ZET-Speech-Demo/.
|
[
"cs.SD",
"cs.CL",
"eess.AS"
] | false |
2305.13905
|
2023-05-23T10:28:41Z
|
EfficientSpeech: An On-Device Text to Speech Model
|
[
"Rowel Atienza"
] |
State of the art (SOTA) neural text to speech (TTS) models can generate
natural-sounding synthetic voices. These models are characterized by large
memory footprints and substantial number of operations due to the long-standing
focus on speech quality with cloud inference in mind. Neural TTS models are
generally not designed to perform standalone speech syntheses on
resource-constrained and no Internet access edge devices. In this work, an
efficient neural TTS called EfficientSpeech that synthesizes speech on an ARM
CPU in real-time is proposed. EfficientSpeech uses a shallow non-autoregressive
pyramid-structure transformer forming a U-Network. EfficientSpeech has 266k
parameters and consumes 90 MFLOPS only or about 1% of the size and amount of
computation in modern compact models such as Mixer-TTS. EfficientSpeech
achieves an average mel generation real-time factor of 104.3 on an RPi4. Human
evaluation shows only a slight degradation in audio quality as compared to
FastSpeech2.
|
[
"eess.AS",
"cs.CL",
"cs.SD"
] | false |
2305.14042
|
2023-05-23T13:13:48Z
|
Improving speech translation by fusing speech and text
|
[
"Wenbiao Yin",
"Zhicheng Liu",
"Chengqi Zhao",
"Tao Wang",
"Jian Tong",
"Rong Ye"
] |
In speech translation, leveraging multimodal data to improve model
performance and address limitations of individual modalities has shown
significant effectiveness. In this paper, we harness the complementary
strengths of speech and text, which are disparate modalities. We observe three
levels of modality gap between them, denoted by Modal input representation,
Modal semantic, and Modal hidden states. To tackle these gaps, we propose
\textbf{F}use-\textbf{S}peech-\textbf{T}ext (\textbf{FST}), a cross-modal model
which supports three distinct input modalities for translation: speech, text,
and fused speech-text. We leverage multiple techniques for cross-modal
alignment and conduct a comprehensive analysis to assess its impact on speech
translation, machine translation, and fused speech-text translation. We
evaluate FST on MuST-C, GigaST, and newstest benchmark. Experiments show that
the proposed FST achieves an average 34.0 BLEU on MuST-C
En$\rightarrow$De/Es/Fr (vs SOTA +1.1 BLEU). Further experiments demonstrate
that FST does not degrade on MT task, as observed in prior works. Instead, it
yields an average improvement of 3.2 BLEU over the pre-trained MT model.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.14049
|
2023-05-23T13:25:44Z
|
Rethinking Speech Recognition with A Multimodal Perspective via Acoustic
and Semantic Cooperative Decoding
|
[
"Tian-Hao Zhang",
"Hai-Bo Qin",
"Zhi-Hao Lai",
"Song-Lu Chen",
"Qi Liu",
"Feng Chen",
"Xinyuan Qian",
"Xu-Cheng Yin"
] |
Attention-based encoder-decoder (AED) models have shown impressive
performance in ASR. However, most existing AED methods neglect to
simultaneously leverage both acoustic and semantic features in decoder, which
is crucial for generating more accurate and informative semantic states. In
this paper, we propose an Acoustic and Semantic Cooperative Decoder (ASCD) for
ASR. In particular, unlike vanilla decoders that process acoustic and semantic
features in two separate stages, ASCD integrates them cooperatively. To prevent
information leakage during training, we design a Causal Multimodal Mask.
Moreover, a variant Semi-ASCD is proposed to balance accuracy and computational
cost. Our proposal is evaluated on the publicly available AISHELL-1 and
aidatatang_200zh datasets using Transformer, Conformer, and Branchformer as
encoders, respectively. The experimental results show that ASCD significantly
improves the performance by leveraging both the acoustic and semantic
information cooperatively.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.14071
|
2023-05-23T13:50:06Z
|
Disentangled Variational Autoencoder for Emotion Recognition in
Conversations
|
[
"Kailai Yang",
"Tianlin Zhang",
"Sophia Ananiadou"
] |
In Emotion Recognition in Conversations (ERC), the emotions of target
utterances are closely dependent on their context. Therefore, existing works
train the model to generate the response of the target utterance, which aims to
recognise emotions leveraging contextual information. However, adjacent
response generation ignores long-range dependencies and provides limited
affective information in many cases. In addition, most ERC models learn a
unified distributed representation for each utterance, which lacks
interpretability and robustness. To address these issues, we propose a
VAD-disentangled Variational AutoEncoder (VAD-VAE), which first introduces a
target utterance reconstruction task based on Variational Autoencoder, then
disentangles three affect representations Valence-Arousal-Dominance (VAD) from
the latent space. We also enhance the disentangled representations by
introducing VAD supervision signals from a sentiment lexicon and minimising the
mutual information between VAD distributions. Experiments show that VAD-VAE
outperforms the state-of-the-art model on two datasets. Further analysis proves
the effectiveness of each proposed module and the quality of disentangled VAD
representations. The code is available at
https://github.com/SteveKGYang/VAD-VAE.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.14106
|
2023-05-23T14:27:16Z
|
Better Zero-Shot Reasoning with Self-Adaptive Prompting
|
[
"Xingchen Wan",
"Ruoxi Sun",
"Hanjun Dai",
"Sercan O. Arik",
"Tomas Pfister"
] |
Modern large language models (LLMs) have demonstrated impressive capabilities
at sophisticated tasks, often through step-by-step reasoning similar to humans.
This is made possible by their strong few and zero-shot abilities -- they can
effectively learn from a handful of handcrafted, completed responses
("in-context examples"), or are prompted to reason spontaneously through
specially designed triggers. Nonetheless, some limitations have been observed.
First, performance in the few-shot setting is sensitive to the choice of
examples, whose design requires significant human effort. Moreover, given the
diverse downstream tasks of LLMs, it may be difficult or laborious to handcraft
per-task labels. Second, while the zero-shot setting does not require
handcrafting, its performance is limited due to the lack of guidance to the
LLMs. To address these limitations, we propose Consistency-based Self-adaptive
Prompting (COSP), a novel prompt design method for LLMs. Requiring neither
handcrafted responses nor ground-truth labels, COSP selects and builds the set
of examples from the LLM zero-shot outputs via carefully designed criteria that
combine consistency, diversity and repetition. In the zero-shot setting for
three different LLMs, we show that using only LLM predictions, COSP improves
performance up to 15% compared to zero-shot baselines and matches or exceeds
few-shot baselines for a range of reasoning tasks.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.14201
|
2023-05-23T16:20:30Z
|
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
|
[
"Tiedong Liu",
"Bryan Kian Hsiang Low"
] |
We introduce Goat, a fine-tuned LLaMA model that significantly outperforms
GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated
dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic
sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the
accuracy achieved by the few-shot PaLM-540B. Surprisingly, Goat can achieve
near-perfect accuracy on large-number addition and subtraction through
supervised fine-tuning only, which is almost impossible with previous
pretrained language models, such as Bloom, OPT, GPT-NeoX, etc. We attribute
Goat's exceptional performance to LLaMA's consistent tokenization of numbers.
To tackle more challenging tasks like large-number multiplication and division,
we propose an approach that classifies tasks based on their learnability, and
subsequently decomposes unlearnable tasks, such as multi-digit multiplication
and division, into a series of learnable tasks by leveraging basic arithmetic
principles. We thoroughly examine the performance of our model, offering a
comprehensive evaluation of the effectiveness of our proposed decomposition
steps. Additionally, Goat-7B can be easily trained using LoRA on a 24GB VRAM
GPU, facilitating reproducibility for other researchers. We release our model,
dataset, and the Python script for dataset generation.
|
[
"cs.LG",
"cs.AI",
"cs.CL"
] | true |
2305.14240
|
2023-05-23T16:56:10Z
|
Revisiting Machine Translation for Cross-lingual Classification
|
[
"Mikel Artetxe",
"Vedanuj Goswami",
"Shruti Bhosale",
"Angela Fan",
"Luke Zettlemoyer"
] |
Machine Translation (MT) has been widely used for cross-lingual
classification, either by translating the test set into English and running
inference with a monolingual model (translate-test), or translating the
training set into the target languages and finetuning a multilingual model
(translate-train). However, most research in the area focuses on the
multilingual models rather than the MT component. We show that, by using a
stronger MT system and mitigating the mismatch between training on original
text and running inference on machine translated text, translate-test can do
substantially better than previously assumed. The optimal approach, however, is
highly task dependent, as we identify various sources of cross-lingual transfer
gap that affect different tasks and approaches differently. Our work calls into
question the dominance of multilingual models for cross-lingual classification,
and prompts to pay more attention to MT-based baselines.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.14546
|
2023-05-23T22:02:55Z
|
On the Transferability of Whisper-based Representations for
"In-the-Wild" Cross-Task Downstream Speech Applications
|
[
"Vamsikrishna Chemudupati",
"Marzieh Tahaei",
"Heitor Guimaraes",
"Arthur Pimentel",
"Anderson Avila",
"Mehdi Rezagholizadeh",
"Boxing Chen",
"Tiago Falk"
] |
Large self-supervised pre-trained speech models have achieved remarkable
success across various speech-processing tasks. The self-supervised training of
these models leads to universal speech representations that can be used for
different downstream tasks, ranging from automatic speech recognition (ASR) to
speaker identification. Recently, Whisper, a transformer-based model was
proposed and trained on large amount of weakly supervised data for ASR; it
outperformed several state-of-the-art self-supervised models. Given the
superiority of Whisper for ASR, in this paper we explore the transferability of
the representation for four other speech tasks in SUPERB benchmark. Moreover,
we explore the robustness of Whisper representation for ``in the wild'' tasks
where speech is corrupted by environment noise and room reverberation.
Experimental results show Whisper achieves promising results across tasks and
environmental conditions, thus showing potential for cross-task real-world
deployment.
|
[
"eess.AS",
"cs.CL",
"cs.LG",
"cs.SD"
] | false |
2305.14555
|
2023-05-23T22:30:43Z
|
All Roads Lead to Rome? Exploring the Invariance of Transformers'
Representations
|
[
"Yuxin Ren",
"Qipeng Guo",
"Zhijing Jin",
"Shauli Ravfogel",
"Mrinmaya Sachan",
"Bernhard Schölkopf",
"Ryan Cotterell"
] |
Transformer models bring propelling advances in various NLP tasks, thus
inducing lots of interpretability research on the learned representations of
the models. However, we raise a fundamental question regarding the reliability
of the representations. Specifically, we investigate whether transformers learn
essentially isomorphic representation spaces, or those that are sensitive to
the random seeds in their pretraining process. In this work, we formulate the
Bijection Hypothesis, which suggests the use of bijective methods to align
different models' representation spaces. We propose a model based on invertible
neural networks, BERT-INN, to learn the bijection more effectively than other
existing bijective methods such as the canonical correlation analysis (CCA). We
show the advantage of BERT-INN both theoretically and through extensive
experiments, and apply it to align the reproduced BERT embeddings to draw
insights that are meaningful to the interpretability research. Our code is at
https://github.com/twinkle0331/BERT-similarity.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.16335
|
2023-05-23T12:43:40Z
|
Robust Representation Learning with Reliable Pseudo-labels Generation
via Self-Adaptive Optimal Transport for Short Text Clustering
|
[
"Xiaolin Zheng",
"Mengling Hu",
"Weiming Liu",
"Chaochao Chen",
"Xinting Liao"
] |
Short text clustering is challenging since it takes imbalanced and noisy data
as inputs. Existing approaches cannot solve this problem well, since (1) they
are prone to obtain degenerate solutions especially on heavy imbalanced
datasets, and (2) they are vulnerable to noises. To tackle the above issues, we
propose a Robust Short Text Clustering (RSTC) model to improve robustness
against imbalanced and noisy data. RSTC includes two modules, i.e.,
pseudo-label generation module and robust representation learning module. The
former generates pseudo-labels to provide supervision for the later, which
contributes to more robust representations and correctly separated clusters. To
provide robustness against the imbalance in data, we propose self-adaptive
optimal transport in the pseudo-label generation module. To improve robustness
against the noise in data, we further introduce both class-wise and
instance-wise contrastive learning in the robust representation learning
module. Our empirical studies on eight short text clustering datasets
demonstrate that RSTC significantly outperforms the state-of-the-art models.
The code is available at: https://github.com/hmllmh/RSTC.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.13678
|
2023-05-23T04:37:18Z
|
Enhancing Accuracy and Robustness through Adversarial Training in Class
Incremental Continual Learning
|
[
"Minchan Kwon",
"Kangil Kim"
] |
In real life, adversarial attack to deep learning models is a fatal security
issue. However, the issue has been rarely discussed in a widely used
class-incremental continual learning (CICL). In this paper, we address problems
of applying adversarial training to CICL, which is well-known defense method
against adversarial attack. A well-known problem of CICL is class-imbalance
that biases a model to the current task by a few samples of previous tasks.
Meeting with the adversarial training, the imbalance causes another imbalance
of attack trials over tasks. Lacking clean data of a minority class by the
class-imbalance and increasing of attack trials from a majority class by the
secondary imbalance, adversarial training distorts optimal decision boundaries.
The distortion eventually decreases both accuracy and robustness than
adversarial training. To exclude the effects, we propose a straightforward but
significantly effective method, External Adversarial Training (EAT) which can
be applied to methods using experience replay. This method conduct adversarial
training to an auxiliary external model for the current task data at each time
step, and applies generated adversarial examples to train the target model. We
verify the effects on a toy problem and show significance on CICL benchmarks of
image classification. We expect that the results will be used as the first
baseline for robustness research of CICL.
|
[
"cs.LG"
] | false |
2305.13804
|
2023-05-23T08:16:44Z
|
Offline Experience Replay for Continual Offline Reinforcement Learning
|
[
"Sibo Gai",
"Donglin Wang",
"Li He"
] |
The capability of continuously learning new skills via a sequence of
pre-collected offline datasets is desired for an agent. However, consecutively
learning a sequence of offline tasks likely leads to the catastrophic
forgetting issue under resource-limited scenarios. In this paper, we formulate
a new setting, continual offline reinforcement learning (CORL), where an agent
learns a sequence of offline reinforcement learning tasks and pursues good
performance on all learned tasks with a small replay buffer without exploring
any of the environments of all the sequential tasks. For consistently learning
on all sequential tasks, an agent requires acquiring new knowledge and
meanwhile preserving old knowledge in an offline manner. To this end, we
introduced continual learning algorithms and experimentally found experience
replay (ER) to be the most suitable algorithm for the CORL problem. However, we
observe that introducing ER into CORL encounters a new distribution shift
problem: the mismatch between the experiences in the replay buffer and
trajectories from the learned policy. To address such an issue, we propose a
new model-based experience selection (MBES) scheme to build the replay buffer,
where a transition model is learned to approximate the state distribution. This
model is used to bridge the distribution bias between the replay buffer and the
learned model by filtering the data from offline data that most closely
resembles the learned model for storage. Moreover, in order to enhance the
ability on learning new tasks, we retrofit the experience replay method with a
new dual behavior cloning (DBC) architecture to avoid the disturbance of
behavior-cloning loss on the Q-learning process. In general, we call our
algorithm offline experience replay (OER). Extensive experiments demonstrate
that our OER method outperforms SOTA baselines in widely-used Mujoco
environments.
|
[
"cs.LG"
] | false |
2305.13871
|
2023-05-23T09:46:54Z
|
Improving Heterogeneous Model Reuse by Density Estimation
|
[
"Anke Tang",
"Yong Luo",
"Han Hu",
"Fengxiang He",
"Kehua Su",
"Bo Du",
"Yixin Chen",
"Dacheng Tao"
] |
This paper studies multiparty learning, aiming to learn a model using the
private data of different participants. Model reuse is a promising solution for
multiparty learning, assuming that a local model has been trained for each
party. Considering the potential sample selection bias among different parties,
some heterogeneous model reuse approaches have been developed. However,
although pre-trained local classifiers are utilized in these approaches, the
characteristics of the local data are not well exploited. This motivates us to
estimate the density of local data and design an auxiliary model together with
the local classifiers for reuse. To address the scenarios where some local
models are not well pre-trained, we further design a multiparty cross-entropy
loss for calibration. Upon existing works, we address a challenging problem of
heterogeneous model reuse from a decision theory perspective and take advantage
of recent advances in density estimation. Experimental results on both
synthetic and benchmark data demonstrate the superiority of the proposed
method.
|
[
"cs.LG"
] | false |
2305.14113
|
2023-05-23T14:37:43Z
|
On the Size and Approximation Error of Distilled Sets
|
[
"Alaa Maalouf",
"Murad Tukan",
"Noel Loo",
"Ramin Hasani",
"Mathias Lechner",
"Daniela Rus"
] |
Dataset Distillation is the task of synthesizing small datasets from large
ones while still retaining comparable predictive accuracy to the original
uncompressed dataset. Despite significant empirical progress in recent years,
there is little understanding of the theoretical limitations/guarantees of
dataset distillation, specifically, what excess risk is achieved by
distillation compared to the original dataset, and how large are distilled
datasets? In this work, we take a theoretical view on kernel ridge regression
(KRR) based methods of dataset distillation such as Kernel Inducing Points. By
transforming ridge regression in random Fourier features (RFF) space, we
provide the first proof of the existence of small (size) distilled datasets and
their corresponding excess risk for shift-invariant kernels. We prove that a
small set of instances exists in the original input space such that its
solution in the RFF space coincides with the solution of the original data. We
further show that a KRR solution can be generated using this distilled set of
instances which gives an approximation towards the KRR solution optimized on
the full input data. The size of this set is linear in the dimension of the RFF
space of the input set or alternatively near linear in the number of effective
degrees of freedom, which is a function of the kernel, number of datapoints,
and the regularization parameter $\lambda$. The error bound of this distilled
set is also a function of $\lambda$. We verify our bounds analytically and
empirically.
|
[
"cs.LG"
] | false |
2305.14115
|
2023-05-23T14:38:33Z
|
RLBoost: Boosting Supervised Models using Deep Reinforcement Learning
|
[
"Eloy Anguiano Batanero",
"Ángela Fernández Pascual",
"Álvaro Barbero Jiménez"
] |
Data quality or data evaluation is sometimes a task as important as
collecting a large volume of data when it comes to generating accurate
artificial intelligence models. In fact, being able to evaluate the data can
lead to a larger database that is better suited to a particular problem because
we have the ability to filter out data obtained automatically of dubious
quality. In this paper we present RLBoost, an algorithm that uses deep
reinforcement learning strategies to evaluate a particular dataset and obtain a
model capable of estimating the quality of any new data in order to improve the
final predictive quality of a supervised learning model. This solution has the
advantage that of being agnostic regarding the supervised model used and,
through multi-attention strategies, takes into account the data in its context
and not only individually. The results of the article show that this model
obtains better and more stable results than other state-of-the-art algorithms
such as LOO, DataShapley or DVRL.
|
[
"cs.LG"
] | false |
2305.14216
|
2023-05-23T16:33:55Z
|
Constrained Proximal Policy Optimization
|
[
"Chengbin Xuan",
"Feng Zhang",
"Faliang Yin",
"Hak-Keung Lam"
] |
The problem of constrained reinforcement learning (CRL) holds significant
importance as it provides a framework for addressing critical safety
satisfaction concerns in the field of reinforcement learning (RL). However,
with the introduction of constraint satisfaction, the current CRL methods
necessitate the utilization of second-order optimization or primal-dual
frameworks with additional Lagrangian multipliers, resulting in increased
complexity and inefficiency during implementation. To address these issues, we
propose a novel first-order feasible method named Constrained Proximal Policy
Optimization (CPPO). By treating the CRL problem as a probabilistic inference
problem, our approach integrates the Expectation-Maximization framework to
solve it through two steps: 1) calculating the optimal policy distribution
within the feasible region (E-step), and 2) conducting a first-order update to
adjust the current policy towards the optimal policy obtained in the E-step
(M-step). We establish the relationship between the probability ratios and KL
divergence to convert the E-step into a convex optimization problem.
Furthermore, we develop an iterative heuristic algorithm from a geometric
perspective to solve this problem. Additionally, we introduce a conservative
update mechanism to overcome the constraint violation issue that occurs in the
existing feasible region method. Empirical evaluations conducted in complex and
uncertain environments validate the effectiveness of our proposed method, as it
performs at least as well as other baselines.
|
[
"cs.LG"
] | false |
2305.14244
|
2023-05-23T16:59:20Z
|
Spatial-temporal Prompt Learning for Federated Weather Forecasting
|
[
"Shengchao Chen",
"Guodong Long",
"Tao Shen",
"Tianyi Zhou",
"Jing Jiang"
] |
Federated weather forecasting is a promising collaborative learning framework
for analyzing meteorological data across participants from different countries
and regions, thus embodying a global-scale real-time weather data predictive
analytics platform to tackle climate change. This paper is to model the
meteorological data in a federated setting where many distributed low-resourced
sensors are deployed in different locations. Specifically, we model the
spatial-temporal weather data into a federated prompt learning framework that
leverages lightweight prompts to share meaningful representation and structural
knowledge among participants. Prompts-based communication allows the server to
establish the structural topology relationships among participants and further
explore the complex spatial-temporal correlations without transmitting private
data while mitigating communication overhead. Moreover, in addition to a
globally shared large model at the server, our proposed method enables each
participant to acquire a personalized model that is highly customized to tackle
climate changes in a specific geographic area. We have demonstrated the
effectiveness of our method on classical weather forecasting tasks by utilizing
three spatial-temporal multivariate time-series weather data.
|
[
"cs.LG"
] | false |
2305.14314
|
2023-05-23T17:50:33Z
|
QLoRA: Efficient Finetuning of Quantized LLMs
|
[
"Tim Dettmers",
"Artidoro Pagnoni",
"Ari Holtzman",
"Luke Zettlemoyer"
] |
We present QLoRA, an efficient finetuning approach that reduces memory usage
enough to finetune a 65B parameter model on a single 48GB GPU while preserving
full 16-bit finetuning task performance. QLoRA backpropagates gradients through
a frozen, 4-bit quantized pretrained language model into Low Rank
Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all
previous openly released models on the Vicuna benchmark, reaching 99.3% of the
performance level of ChatGPT while only requiring 24 hours of finetuning on a
single GPU. QLoRA introduces a number of innovations to save memory without
sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is
information theoretically optimal for normally distributed weights (b) double
quantization to reduce the average memory footprint by quantizing the
quantization constants, and (c) paged optimziers to manage memory spikes. We
use QLoRA to finetune more than 1,000 models, providing a detailed analysis of
instruction following and chatbot performance across 8 instruction datasets,
multiple model types (LLaMA, T5), and model scales that would be infeasible to
run with regular finetuning (e.g. 33B and 65B parameter models). Our results
show that QLoRA finetuning on a small high-quality dataset leads to
state-of-the-art results, even when using smaller models than the previous
SoTA. We provide a detailed analysis of chatbot performance based on both human
and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable
alternative to human evaluation. Furthermore, we find that current chatbot
benchmarks are not trustworthy to accurately evaluate the performance levels of
chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to
ChatGPT. We release all of our models and code, including CUDA kernels for
4-bit training.
|
[
"cs.LG"
] | true |
2305.13560
|
2023-05-23T00:15:56Z
|
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!
|
[
"Sayak Chakrabarty",
"Konstantin Makarychev"
] |
We show that a simple single-pass semi-streaming variant of the Pivot
algorithm for Correlation Clustering gives a (3 + {\epsilon})-approximation
using O(n/{\epsilon}) words of memory. This is a slight improvement over the
recent results of Cambus, Kuhn, Lindy, Pai, and Uitto, who gave a (3 +
{\epsilon})-approximation using O(n log n) words of memory, and Behnezhad,
Charikar, Ma, and Tan, who gave a 5-approximation using O(n) words of memory.
One of the main contributions of this paper is that both the algorithm and its
analysis are very simple, and also the algorithm is easy to implement.
|
[
"cs.DS",
"cs.LG"
] | false |
2305.13573
|
2023-05-23T01:05:34Z
|
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
|
[
"Sheng Tian",
"Jihai Dong",
"Jintang Li",
"Wenlong Zhao",
"Xiaolong Xu",
"Baokun wang",
"Bowen Song",
"Changhua Meng",
"Tianyi Zhang",
"Liang Chen"
] |
Anomaly detection aims to distinguish abnormal instances that deviate
significantly from the majority of benign ones. As instances that appear in the
real world are naturally connected and can be represented with graphs, graph
neural networks become increasingly popular in tackling the anomaly detection
problem. Despite the promising results, research on anomaly detection has
almost exclusively focused on static graphs while the mining of anomalous
patterns from dynamic graphs is rarely studied but has significant application
value. In addition, anomaly detection is typically tackled from semi-supervised
perspectives due to the lack of sufficient labeled data. However, most proposed
methods are limited to merely exploiting labeled data, leaving a large number
of unlabeled samples unexplored. In this work, we present semi-supervised
anomaly detection (SAD), an end-to-end framework for anomaly detection on
dynamic graphs. By a combination of a time-equipped memory bank and a
pseudo-label contrastive learning module, SAD is able to fully exploit the
potential of large unlabeled samples and uncover underlying anomalies on
evolving graph streams. Extensive experiments on four real-world datasets
demonstrate that SAD efficiently discovers anomalies from dynamic graphs and
outperforms existing advanced methods even when provided with only little
labeled data.
|
[
"cs.LG",
"cs.SI"
] | false |
2305.13634
|
2023-05-23T03:01:26Z
|
SMAP: A Novel Heterogeneous Information Framework for Scenario-based
Optimal Model Assignment
|
[
"Zekun Qiu",
"Zhipu Xie",
"Zehua Ji",
"Yuhao Mao",
"Ke Cheng"
] |
The increasing maturity of big data applications has led to a proliferation
of models targeting the same objectives within the same scenarios and datasets.
However, selecting the most suitable model that considers model's features
while taking specific requirements and constraints into account still poses a
significant challenge. Existing methods have focused on worker-task assignments
based on crowdsourcing, they neglect the scenario-dataset-model assignment
problem. To address this challenge, a new problem named the Scenario-based
Optimal Model Assignment (SOMA) problem is introduced and a novel framework
entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a
heterogeneous information framework that can integrate various types of
information to intelligently select a suitable dataset and allocate the optimal
model for a specific scenario. To comprehensively evaluate models, a new score
function that utilizes multi-head attention mechanisms is proposed. Moreover, a
novel memory mechanism named the mnemonic center is developed to store the
matched heterogeneous information and prevent duplicate matching. Six popular
traffic scenarios are selected as study cases and extensive experiments are
conducted on a dataset to verify the effectiveness and efficiency of SMAP and
the score function.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.13644
|
2023-05-23T03:40:26Z
|
Physics-Assisted Reduced-Order Modeling for Identifying Dominant
Features of Transonic Buffet
|
[
"Jing Wang",
"Hairun Xie",
"Miao Zhang",
"Hui Xu"
] |
Transonic buffet is a flow instability phenomenon that arises from the
interaction between the shock wave and the separated boundary layer. This flow
phenomenon is considered to be highly detrimental during flight and poses a
significant risk to the structural strength and fatigue life of aircraft. Up to
now, there has been a lack of an accurate, efficient, and intuitive metric to
predict buffet and impose a feasible constraint on aerodynamic design. In this
paper, a Physics-Assisted Variational Autoencoder (PAVAE) is proposed to
identify dominant features of transonic buffet, which combines unsupervised
reduced-order modeling with additional physical information embedded via a
buffet classifier. Specifically, four models with various weights adjusting the
contribution of the classifier are trained, so as to investigate the impact of
buffet information on the latent space. Statistical results reveal that buffet
state can be determined exactly with just one latent space when a proper weight
of classifier is chosen. The dominant latent space further reveals a strong
relevance with the key flow features located in the boundary layers downstream
of shock. Based on this identification, the displacement thickness at 80%
chordwise location is proposed as a metric for buffet prediction. This metric
achieves an accuracy of 98.5% in buffet state classification, which is more
reliable than the existing separation metric used in design. The proposed
method integrates the benefits of feature extraction, flow reconstruction, and
buffet prediction into a unified framework, demonstrating its potential in
low-dimensional representations of high-dimensional flow data and interpreting
the "black box" neural network.
|
[
"physics.flu-dyn",
"cs.LG"
] | false |
2305.13646
|
2023-05-23T03:41:45Z
|
An Autoencoder-based Snow Drought Index
|
[
"Sinan Rasiya Koya",
"Kanak Kanti Kar",
"Shivendra Srivastava",
"Tsegaye Tadesse",
"Mark Svoboda",
"Tirthankar Roy"
] |
In several regions across the globe, snow has a significant impact on
hydrology. The amounts of water that infiltrate the ground and flow as runoff
are driven by the melting of snow. Therefore, it is crucial to study the
magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow
storage, can drastically impact the water supplies in basins where snow
predominates, such as in the western United States. Hence, it is important to
detect the time and severity of snow droughts efficiently. We propose Snow
Drought Response Index or SnoDRI, a novel indicator that could be used to
identify and quantify snow drought occurrences. Our index is calculated using
cutting-edge ML algorithms from various snow-related variables. The
self-supervised learning of an autoencoder is combined with mutual information
in the model. In this study, we use random forests for feature extraction for
SnoDRI and assess the importance of each variable. We use reanalysis data
(NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy
of the new snow drought index. We evaluate the index by confirming the
coincidence of its interpretation and the actual snow drought incidents.
|
[
"cs.LG",
"physics.ao-ph"
] | false |
2305.13656
|
2023-05-23T03:59:21Z
|
Link Prediction without Graph Neural Networks
|
[
"Zexi Huang",
"Mert Kosan",
"Arlei Silva",
"Ambuj Singh"
] |
Link prediction, which consists of predicting edges based on graph features,
is a fundamental task in many graph applications. As for several related
problems, Graph Neural Networks (GNNs), which are based on an attribute-centric
message-passing paradigm, have become the predominant framework for link
prediction. GNNs have consistently outperformed traditional topology-based
heuristics, but what contributes to their performance? Are there simpler
approaches that achieve comparable or better results? To answer these
questions, we first identify important limitations in how GNN-based link
prediction methods handle the intrinsic class imbalance of the problem -- due
to the graph sparsity -- in their training and evaluation. Moreover, we propose
Gelato, a novel topology-centric framework that applies a topological heuristic
to a graph enhanced by attribute information via graph learning. Our model is
trained end-to-end with an N-pair loss on an unbiased training set to address
class imbalance. Experiments show that Gelato is 145% more accurate, trains 11
times faster, infers 6,000 times faster, and has less than half of the
trainable parameters compared to state-of-the-art GNNs for link prediction.
|
[
"cs.LG",
"cs.SI"
] | false |
2305.13715
|
2023-05-23T06:06:45Z
|
Covariate balancing using the integral probability metric for causal
inference
|
[
"Insung Kong",
"Yuha Park",
"Joonhyuk Jung",
"Kwonsang Lee",
"Yongdai Kim"
] |
Weighting methods in causal inference have been widely used to achieve a
desirable level of covariate balancing. However, the existing weighting methods
have desirable theoretical properties only when a certain model, either the
propensity score or outcome regression model, is correctly specified. In
addition, the corresponding estimators do not behave well for finite samples
due to large variance even when the model is correctly specified. In this
paper, we consider to use the integral probability metric (IPM), which is a
metric between two probability measures, for covariate balancing. Optimal
weights are determined so that weighted empirical distributions for the treated
and control groups have the smallest IPM value for a given set of
discriminators. We prove that the corresponding estimator can be consistent
without correctly specifying any model (neither the propensity score nor the
outcome regression model). In addition, we empirically show that our proposed
method outperforms existing weighting methods with large margins for finite
samples.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.13741
|
2023-05-23T06:51:51Z
|
L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement
Learning
|
[
"Kibeom Kim",
"Hyundo Lee",
"Min Whoo Lee",
"Moonheon Lee",
"Minsu Lee",
"Byoung-Tak Zhang"
] |
Tasks that involve interaction with various targets are called multi-target
tasks. When applying general reinforcement learning approaches for such tasks,
certain targets that are difficult to access or interact with may be neglected
throughout the course of training - a predicament we call Under-explored Target
Problem (UTP). To address this problem, we propose L-SA (Learning by adaptive
Sampling and Active querying) framework that includes adaptive sampling and
active querying. In the L-SA framework, adaptive sampling dynamically samples
targets with the highest increase of success rates at a high proportion,
resulting in curricular learning from easy to hard targets. Active querying
prompts the agent to interact more frequently with under-explored targets that
need more experience or exploration. Our experimental results on visual
navigation tasks show that the L-SA framework improves sample efficiency as
well as success rates on various multi-target tasks with UTP. Also, it is
experimentally demonstrated that the cyclic relationship between adaptive
sampling and active querying effectively improves the sample richness of
under-explored targets and alleviates UTP.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.13768
|
2023-05-23T07:32:37Z
|
One-step differentiation of iterative algorithms
|
[
"Jérôme Bolte",
"Edouard Pauwels",
"Samuel Vaiter"
] |
In appropriate frameworks, automatic differentiation is transparent to the
user at the cost of being a significant computational burden when the number of
operations is large. For iterative algorithms, implicit differentiation
alleviates this issue but requires custom implementation of Jacobian
evaluation. In this paper, we study one-step differentiation, also known as
Jacobian-free backpropagation, a method as easy as automatic differentiation
and as performant as implicit differentiation for fast algorithms (e.g.,
superlinear optimization methods). We provide a complete theoretical
approximation analysis with specific examples (Newton's method, gradient
descent) along with its consequences in bilevel optimization. Several numerical
examples illustrate the well-foundness of the one-step estimator.
|
[
"math.OC",
"cs.LG"
] | false |
2305.13854
|
2023-05-23T09:22:33Z
|
The Evolution of Distributed Systems for Graph Neural Networks and their
Origin in Graph Processing and Deep Learning: A Survey
|
[
"Jana Vatter",
"Ruben Mayer",
"Hans-Arno Jacobsen"
] |
Graph Neural Networks (GNNs) are an emerging research field. This specialized
Deep Neural Network (DNN) architecture is capable of processing graph
structured data and bridges the gap between graph processing and Deep Learning
(DL). As graphs are everywhere, GNNs can be applied to various domains
including recommendation systems, computer vision, natural language processing,
biology and chemistry. With the rapid growing size of real world graphs, the
need for efficient and scalable GNN training solutions has come. Consequently,
many works proposing GNN systems have emerged throughout the past few years.
However, there is an acute lack of overview, categorization and comparison of
such systems. We aim to fill this gap by summarizing and categorizing important
methods and techniques for large-scale GNN solutions. In addition, we establish
connections between GNN systems, graph processing systems and DL systems.
|
[
"cs.DC",
"cs.LG"
] | false |
2305.13875
|
2023-05-23T09:51:18Z
|
Fair Oversampling Technique using Heterogeneous Clusters
|
[
"Ryosuke Sonoda"
] |
Class imbalance and group (e.g., race, gender, and age) imbalance are
acknowledged as two reasons in data that hinder the trade-off between fairness
and utility of machine learning classifiers. Existing techniques have jointly
addressed issues regarding class imbalance and group imbalance by proposing
fair over-sampling techniques. Unlike the common oversampling techniques, which
only address class imbalance, fair oversampling techniques significantly
improve the abovementioned trade-off, as they can also address group imbalance.
However, if the size of the original clusters is too small, these techniques
may cause classifier overfitting. To address this problem, we herein develop a
fair oversampling technique using data from heterogeneous clusters. The
proposed technique generates synthetic data that have class-mix features or
group-mix features to make classifiers robust to overfitting. Moreover, we
develop an interpolation method that can enhance the validity of generated
synthetic data by considering the original cluster distribution and data noise.
Finally, we conduct experiments on five realistic datasets and three
classifiers, and the experimental results demonstrate the effectiveness of the
proposed technique in terms of fairness and utility.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.13878
|
2023-05-23T09:58:48Z
|
Fair Differentially Private Federated Learning Framework
|
[
"Ayush K. Varshney",
"Sonakshi Garg",
"Arka Ghosh",
"Sargam Gupta"
] |
Federated learning (FL) is a distributed machine learning strategy that
enables participants to collaborate and train a shared model without sharing
their individual datasets. Privacy and fairness are crucial considerations in
FL. While FL promotes privacy by minimizing the amount of user data stored on
central servers, it still poses privacy risks that need to be addressed.
Industry standards such as differential privacy, secure multi-party
computation, homomorphic encryption, and secure aggregation protocols are
followed to ensure privacy in FL. Fairness is also a critical issue in FL, as
models can inherit biases present in local datasets, leading to unfair
predictions. Balancing privacy and fairness in FL is a challenge, as privacy
requires protecting user data while fairness requires representative training
data. This paper presents a "Fair Differentially Private Federated Learning
Framework" that addresses the challenges of generating a fair global model
without validation data and creating a globally private differential model. The
framework employs clipping techniques for biased model updates and Gaussian
mechanisms for differential privacy. The paper also reviews related works on
privacy and fairness in FL, highlighting recent advancements and approaches to
mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires
careful consideration of specific contexts and requirements, taking into
account the latest developments in industry standards and techniques.
|
[
"cs.LG",
"cs.CY"
] | false |
2305.13911
|
2023-05-23T10:33:52Z
|
A Deep Learning Approach for Generating Soft Range Information from RF
Data
|
[
"Yuxiao Li",
"Santiago Mazuelas",
"Yuan Shen"
] |
Radio frequency (RF)-based techniques are widely adopted for indoor
localization despite the challenges in extracting sufficient information from
measurements. Soft range information (SRI) offers a promising alternative for
highly accurate localization that gives all probable range values rather than a
single estimate of distance. We propose a deep learning approach to generate
accurate SRI from RF measurements. In particular, the proposed approach is
implemented by a network with two neural modules and conducts the generation
directly from raw data. Extensive experiments on a case study with two public
datasets are conducted to quantify the efficiency in different indoor
localization tasks. The results show that the proposed approach can generate
highly accurate SRI, and significantly outperforms conventional techniques in
both non-line-of-sight (NLOS) detection and ranging error mitigation.
|
[
"cs.LG",
"eess.SP"
] | false |
2305.13926
|
2023-05-23T10:52:37Z
|
Clustering Indices based Automatic Classification Model Selection
|
[
"Sudarsun Santhiappan",
"Nitin Shravan",
"Balaraman Ravindran"
] |
Classification model selection is a process of identifying a suitable model
class for a given classification task on a dataset. Traditionally, model
selection is based on cross-validation, meta-learning, and user preferences,
which are often time-consuming and resource-intensive. The performance of any
machine learning classification task depends on the choice of the model class,
the learning algorithm, and the dataset's characteristics. Our work proposes a
novel method for automatic classification model selection from a set of
candidate model classes by determining the empirical model-fitness for a
dataset based only on its clustering indices. Clustering Indices measure the
ability of a clustering algorithm to induce good quality neighborhoods with
similar data characteristics. We propose a regression task for a given model
class, where the clustering indices of a given dataset form the features and
the dependent variable represents the expected classification performance. We
compute the dataset clustering indices and directly predict the expected
classification performance using the learned regressor for each candidate model
class to recommend a suitable model class for dataset classification. We
evaluate our model selection method through cross-validation with 60 publicly
available binary class datasets and show that our top3 model recommendation is
accurate for over 45 of 60 datasets. We also propose an end-to-end Automated ML
system for data classification based on our model selection method. We evaluate
our end-to-end system against popular commercial and noncommercial Automated ML
systems using a different collection of 25 public domain binary class datasets.
We show that the proposed system outperforms other methods with an excellent
average rank of 1.68.
|
[
"cs.LG",
"cs.AI",
"I.5.3; I.2.1; I.2.6; I.2.8"
] | false |
2305.13987
|
2023-05-23T12:12:21Z
|
On Structural Expressive Power of Graph Transformers
|
[
"Wenhao Zhu",
"Tianyu Wen",
"Guojie Song",
"Liang Wang",
"Bo Zheng"
] |
Graph Transformer has recently received wide attention in the research
community with its outstanding performance, yet its structural expressive power
has not been well analyzed. Inspired by the connections between
Weisfeiler-Lehman (WL) graph isomorphism test and graph neural network (GNN),
we introduce \textbf{SEG-WL test} (\textbf{S}tructural \textbf{E}ncoding
enhanced \textbf{G}lobal \textbf{W}eisfeiler-\textbf{L}ehman test), a
generalized graph isomorphism test algorithm as a powerful theoretical tool for
exploring the structural discriminative power of graph Transformers. We
theoretically prove that the SEG-WL test is an expressivity upper bound on a
wide range of graph Transformers, and the representational power of SEG-WL test
can be approximated by a simple Transformer network arbitrarily under certain
conditions. With the SEG-WL test, we show how graph Transformers' expressive
power is determined by the design of structural encodings, and present
conditions that make the expressivity of graph Transformers beyond WL test and
GNNs. Moreover, motivated by the popular shortest path distance encoding, we
follow the theory-oriented principles and develop a provably stronger
structural encoding method, Shortest Path Induced Subgraph (\textit{SPIS})
encoding. Our theoretical findings provide a novel and practical paradigm for
investigating the expressive power of graph Transformers, and extensive
synthetic and real-world experiments empirically verify the strengths of our
proposed methods.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.14098
|
2023-05-23T14:20:38Z
|
Balancing Explainability-Accuracy of Complex Models
|
[
"Poushali Sengupta",
"Yan Zhang",
"Sabita Maharjan",
"Frank Eliassen"
] |
Explainability of AI models is an important topic that can have a significant
impact in all domains and applications from autonomous driving to healthcare.
The existing approaches to explainable AI (XAI) are mainly limited to simple
machine learning algorithms, and the research regarding the
explainability-accuracy tradeoff is still in its infancy especially when we are
concerned about complex machine learning techniques like neural networks and
deep learning (DL). In this work, we introduce a new approach for complex
models based on the co-relation impact which enhances the explainability
considerably while also ensuring the accuracy at a high level. We propose
approaches for both scenarios of independent features and dependent features.
In addition, we study the uncertainty associated with features and output.
Furthermore, we provide an upper bound of the computation complexity of our
proposed approach for the dependent features. The complexity bound depends on
the order of logarithmic of the number of observations which provides a
reliable result considering the higher dimension of dependent feature space
with a smaller number of observations.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.14109
|
2023-05-23T14:31:52Z
|
Augmented Random Search for Multi-Objective Bayesian Optimization of
Neural Networks
|
[
"Mark Deutel",
"Georgios Kontes",
"Christopher Mutschler",
"Jürgen Teich"
] |
Deploying Deep Neural Networks (DNNs) on tiny devices is a common trend to
process the increasing amount of sensor data being generated. Multi-objective
optimization approaches can be used to compress DNNs by applying network
pruning and weight quantization to minimize the memory footprint (RAM), the
number of parameters (ROM) and the number of floating point operations (FLOPs)
while maintaining the predictive accuracy. In this paper, we show that existing
multi-objective Bayesian optimization (MOBOpt) approaches can fall short in
finding optimal candidates on the Pareto front and propose a novel solver based
on an ensemble of competing parametric policies trained using an Augmented
Random Search Reinforcement Learning (RL) agent. Our methodology aims at
finding feasible tradeoffs between a DNN's predictive accuracy, memory
consumption on a given target system, and computational complexity. Our
experiments show that we outperform existing MOBOpt approaches consistently on
different data sets and architectures such as ResNet-18 and MobileNetV3.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.14161
|
2023-05-23T15:26:36Z
|
Revisiting Subgradient Method: Complexity and Convergence Beyond
Lipschitz Continuity
|
[
"Xiao Li",
"Lei Zhao",
"Daoli Zhu",
"Anthony Man-Cho So"
] |
The subgradient method is one of the most fundamental algorithmic schemes for
nonsmooth optimization. The existing complexity and convergence results for
this algorithm are mainly derived for Lipschitz continuous objective functions.
In this work, we first extend the typical complexity results for the
subgradient method to convex and weakly convex minimization without assuming
Lipschitz continuity. Specifically, we establish $\mathcal{O}(1/\sqrt{T})$
bound in terms of the suboptimality gap ``$f(x) - f^*$'' for convex case and
$\mathcal{O}(1/{T}^{1/4})$ bound in terms of the gradient of the Moreau
envelope function for weakly convex case. Furthermore, we provide convergence
results for non-Lipschitz convex and weakly convex objective functions using
proper diminishing rules on the step sizes. In particular, when $f$ is convex,
we show $\mathcal{O}(\log(k)/\sqrt{k})$ rate of convergence in terms of the
suboptimality gap. With an additional quadratic growth condition, the rate is
improved to $\mathcal{O}(1/k)$ in terms of the squared distance to the optimal
solution set. When $f$ is weakly convex, asymptotic convergence is derived. The
central idea is that the dynamics of properly chosen step sizes rule fully
controls the movement of the subgradient method, which leads to boundedness of
the iterates, and then a trajectory-based analysis can be conducted to
establish the desired results. To further illustrate the wide applicability of
our framework, we extend the complexity results to the truncated subgradient,
the stochastic subgradient, the incremental subgradient, and the proximal
subgradient methods for non-Lipschitz functions.
|
[
"math.OC",
"cs.LG"
] | false |
2305.14177
|
2023-05-23T15:56:17Z
|
ChemGymRL: An Interactive Framework for Reinforcement Learning for
Digital Chemistry
|
[
"Chris Beeler",
"Sriram Ganapathi Subramanian",
"Kyle Sprague",
"Nouha Chatti",
"Colin Bellinger",
"Mitchell Shahen",
"Nicholas Paquin",
"Mark Baula",
"Amanuel Dawit",
"Zihan Yang",
"Xinkai Li",
"Mark Crowley",
"Isaac Tamblyn"
] |
This paper provides a simulated laboratory for making use of Reinforcement
Learning (RL) for chemical discovery. Since RL is fairly data intensive,
training agents `on-the-fly' by taking actions in the real world is infeasible
and possibly dangerous. Moreover, chemical processing and discovery involves
challenges which are not commonly found in RL benchmarks and therefore offer a
rich space to work in. We introduce a set of highly customizable and
open-source RL environments, ChemGymRL, based on the standard Open AI Gym
template. ChemGymRL supports a series of interconnected virtual chemical
benches where RL agents can operate and train. The paper introduces and details
each of these benches using well-known chemical reactions as illustrative
examples, and trains a set of standard RL algorithms in each of these benches.
Finally, discussion and comparison of the performances of several standard RL
methods are provided in addition to a list of directions for future work as a
vision for the further development and usage of ChemGymRL.
|
[
"cs.LG",
"physics.chem-ph"
] | false |
2305.14406
|
2023-05-23T13:30:35Z
|
Deep Learning based Forecasting: a case study from the online fashion
industry
|
[
"Manuel Kunz",
"Stefan Birr",
"Mones Raslan",
"Lei Ma",
"Zhen Li",
"Adele Gouttes",
"Mateusz Koren",
"Tofigh Naghibi",
"Johannes Stephan",
"Mariia Bulycheva",
"Matthias Grzeschik",
"Armin Kekić",
"Michael Narodovitch",
"Kashif Rasul",
"Julian Sieber",
"Tim Januschowski"
] |
Demand forecasting in the online fashion industry is particularly amendable
to global, data-driven forecasting models because of the industry's set of
particular challenges. These include the volume of data, the irregularity, the
high amount of turn-over in the catalog and the fixed inventory assumption.
While standard deep learning forecasting approaches cater for many of these,
the fixed inventory assumption requires a special treatment via controlling the
relationship between price and demand closely. In this case study, we describe
the data and our modelling approach for this forecasting problem in detail and
present empirical results that highlight the effectiveness of our approach.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.14454
|
2023-05-23T18:26:29Z
|
An Improved Variational Approximate Posterior for the Deep Wishart
Process
|
[
"Sebastian Ober",
"Ben Anson",
"Edward Milsom",
"Laurence Aitchison"
] |
Deep kernel processes are a recently introduced class of deep Bayesian models
that have the flexibility of neural networks, but work entirely with Gram
matrices. They operate by alternately sampling a Gram matrix from a
distribution over positive semi-definite matrices, and applying a deterministic
transformation. When the distribution is chosen to be Wishart, the model is
called a deep Wishart process (DWP). This particular model is of interest
because its prior is equivalent to a deep Gaussian process (DGP) prior, but at
the same time it is invariant to rotational symmetries, leading to a simpler
posterior distribution. Practical inference in the DWP was made possible in
recent work ("A variational approximate posterior for the deep Wishart process"
Ober and Aitchison 2021a) where the authors used a generalisation of the
Bartlett decomposition of the Wishart distribution as the variational
approximate posterior. However, predictive performance in that paper was less
impressive than one might expect, with the DWP only beating a DGP on a few of
the UCI datasets used for comparison. In this paper, we show that further
generalising their distribution to allow linear combinations of rows and
columns in the Bartlett decomposition results in better predictive performance,
while incurring negligible additional computation cost.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.14528
|
2023-05-23T21:10:17Z
|
Basis Function Encoding of Numerical Features in Factorization Machines
for Improved Accuracy
|
[
"Alex Shtoff",
"Elie Abboud",
"Rotem Stram",
"Oren Somekh"
] |
Factorization machine (FM) variants are widely used for large scale real-time
content recommendation systems, since they offer an excellent balance between
model accuracy and low computational costs for training and inference. These
systems are trained on tabular data with both numerical and categorical
columns. Incorporating numerical columns poses a challenge, and they are
typically incorporated using a scalar transformation or binning, which can be
either learned or chosen a-priori. In this work, we provide a systematic and
theoretically-justified way to incorporate numerical features into FM variants
by encoding them into a vector of function values for a set of functions of
one's choice.
We view factorization machines as approximators of segmentized functions,
namely, functions from a field's value to the real numbers, assuming the
remaining fields are assigned some given constants, which we refer to as the
segment. From this perspective, we show that our technique yields a model that
learns segmentized functions of the numerical feature spanned by the set of
functions of one's choice, namely, the spanning coefficients vary between
segments. Hence, to improve model accuracy we advocate the use of functions
known to have strong approximation power, and offer the B-Spline basis due to
its well-known approximation power, availability in software libraries, and
efficiency. Our technique preserves fast training and inference, and requires
only a small modification of the computational graph of an FM model. Therefore,
it is easy to incorporate into an existing system to improve its performance.
Finally, we back our claims with a set of experiments, including synthetic,
performance evaluation on several data-sets, and an A/B test on a real online
advertising system which shows improved performance.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.14543
|
2023-05-23T21:59:13Z
|
DF2M: An Explainable Deep Bayesian Nonparametric Model for
High-Dimensional Functional Time Series
|
[
"Yirui Liu",
"Xinghao Qiao",
"Yulong Pei",
"Liying Wang"
] |
In this paper, we present Deep Functional Factor Model (DF2M), a Bayesian
nonparametric model for analyzing high-dimensional functional time series. The
DF2M makes use of the Indian Buffet Process and the multi-task Gaussian Process
with a deep kernel function to capture non-Markovian and nonlinear temporal
dynamics. Unlike many black-box deep learning models, the DF2M provides an
explainable way to use neural networks by constructing a factor model and
incorporating deep neural networks within the kernel function. Additionally, we
develop a computationally efficient variational inference algorithm for
inferring the DF2M. Empirical results from four real-world datasets demonstrate
that the DF2M offers better explainability and superior predictive accuracy
compared to conventional deep learning models for high-dimensional functional
time series.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.14582
|
2023-05-23T23:43:26Z
|
Interpretation of Time-Series Deep Models: A Survey
|
[
"Ziqi Zhao",
"Yucheng Shi",
"Shushan Wu",
"Fan Yang",
"Wenzhan Song",
"Ninghao Liu"
] |
Deep learning models developed for time-series associated tasks have become
more widely researched nowadays. However, due to the unintuitive nature of
time-series data, the interpretability problem -- where we understand what is
under the hood of these models -- becomes crucial. The advancement of similar
studies in computer vision has given rise to many post-hoc methods, which can
also shed light on how to explain time-series models. In this paper, we present
a wide range of post-hoc interpretation methods for time-series models based on
backpropagation, perturbation, and approximation. We also want to bring focus
onto inherently interpretable models, a novel category of interpretation where
human-understandable information is designed within the models. Furthermore, we
introduce some common evaluation metrics used for the explanations, and propose
several directions of future researches on the time-series interpretability
problem. As a highlight, our work summarizes not only the well-established
interpretation methods, but also a handful of fairly recent and under-developed
techniques, which we hope to capture their essence and spark future endeavours
to innovate and improvise.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.15317
|
2023-05-23T03:50:56Z
|
On the robust learning mixtures of linear regressions
|
[
"Ying Huang",
"Liang Chen"
] |
In this note, we consider the problem of robust learning mixtures of linear
regressions. We connect mixtures of linear regressions and mixtures of
Gaussians with a simple thresholding, so that a quasi-polynomial time algorithm
can be obtained under some mild separation condition. This algorithm has
significantly better robustness than the previous result.
|
[
"stat.ML",
"cs.LG"
] | false |
2306.05375
|
2023-05-23T17:25:51Z
|
Sequential Graph Neural Networks for Source Code Vulnerability
Identification
|
[
"Ammar Ahmed",
"Anwar Said",
"Mudassir Shabbir",
"Xenofon Koutsoukos"
] |
Vulnerability identification constitutes a task of high importance for cyber
security. It is quite helpful for locating and fixing vulnerable functions in
large applications. However, this task is rather challenging owing to the
absence of reliable and adequately managed datasets and learning models.
Existing solutions typically rely on human expertise to annotate datasets or
specify features, which is prone to error. In addition, the learning models
have a high rate of false positives. To bridge this gap, in this paper, we
present a properly curated C/C++ source code vulnerability dataset, denoted as
CVEFunctionGraphEmbeddings (CVEFGE), to aid in developing models. CVEFGE is
automatically crawled from the CVE database, which contains authentic and
publicly disclosed source code vulnerabilities. We also propose a learning
framework based on graph neural networks, denoted SEquential Graph Neural
Network (SEGNN) for learning a large number of code semantic representations.
SEGNN consists of a sequential learning module, graph convolution, pooling, and
fully connected layers. Our evaluations on two datasets and four baseline
methods in a graph classification setting demonstrate state-of-the-art results.
|
[
"cs.CR",
"cs.LG"
] | false |
2305.13824
|
2023-05-23T08:48:54Z
|
Constrained Reinforcement Learning for Dynamic Material Handling
|
[
"Chengpeng Hu",
"Ziming Wang",
"Jialin Liu",
"Junyi Wen",
"Bifei Mao",
"Xin Yao"
] |
As one of the core parts of flexible manufacturing systems, material handling
involves storage and transportation of materials between workstations with
automated vehicles. The improvement in material handling can impulse the
overall efficiency of the manufacturing system. However, the occurrence of
dynamic events during the optimisation of task arrangements poses a challenge
that requires adaptability and effectiveness. In this paper, we aim at the
scheduling of automated guided vehicles for dynamic material handling.
Motivated by some real-world scenarios, unknown new tasks and unexpected
vehicle breakdowns are regarded as dynamic events in our problem. We formulate
the problem as a constrained Markov decision process which takes into account
tardiness and available vehicles as cumulative and instantaneous constraints,
respectively. An adaptive constrained reinforcement learning algorithm that
combines Lagrangian relaxation and invalid action masking, named RCPOM, is
proposed to address the problem with two hybrid constraints. Moreover, a
gym-like dynamic material handling simulator, named DMH-GYM, is developed and
equipped with diverse problem instances, which can be used as benchmarks for
dynamic material handling. Experimental results on the problem instances
demonstrate the outstanding performance of our proposed approach compared with
eight state-of-the-art constrained and non-constrained reinforcement learning
algorithms, and widely used dispatching rules for material handling.
|
[
"cs.LG",
"cs.AI",
"cs.RO"
] | false |
2305.13856
|
2023-05-23T09:23:47Z
|
On the Optimal Batch Size for Byzantine-Robust Distributed Learning
|
[
"Yi-Rui Yang",
"Chang-Wei Shi",
"Wu-Jun Li"
] |
Byzantine-robust distributed learning (BRDL), in which computing devices are
likely to behave abnormally due to accidental failures or malicious attacks,
has recently become a hot research topic. However, even in the independent and
identically distributed (i.i.d.) case, existing BRDL methods will suffer from a
significant drop on model accuracy due to the large variance of stochastic
gradients. Increasing batch sizes is a simple yet effective way to reduce the
variance. However, when the total number of gradient computation is fixed, a
too-large batch size will lead to a too-small iteration number (update number),
which may also degrade the model accuracy. In view of this challenge, we mainly
study the optimal batch size when the total number of gradient computation is
fixed in this work. In particular, we theoretically and empirically show that
when the total number of gradient computation is fixed, the optimal batch size
in BRDL increases with the fraction of Byzantine workers. Therefore, compared
to the case without attacks, the batch size should be set larger when under
Byzantine attacks. However, for existing BRDL methods, large batch sizes will
lead to a drop on model accuracy, even if there is no Byzantine attack. To deal
with this problem, we propose a novel BRDL method, called Byzantine-robust
stochastic gradient descent with normalized momentum (ByzSGDnm), which can
alleviate the drop on model accuracy in large-batch cases. Moreover, we
theoretically prove the convergence of ByzSGDnm for general non-convex cases
under Byzantine attacks. Empirical results show that ByzSGDnm has a comparable
performance to existing BRDL methods under bit-flipping failure, but can
outperform existing BRDL methods under deliberately crafted attacks.
|
[
"cs.LG",
"math.OC",
"stat.ML"
] | false |
2305.13882
|
2023-05-23T10:03:40Z
|
Subsampling Error in Stochastic Gradient Langevin Diffusions
|
[
"Kexin Jin",
"Chenguang Liu",
"Jonas Latz"
] |
The Stochastic Gradient Langevin Dynamics (SGLD) are popularly used to
approximate Bayesian posterior distributions in statistical learning procedures
with large-scale data. As opposed to many usual Markov chain Monte Carlo (MCMC)
algorithms, SGLD is not stationary with respect to the posterior distribution;
two sources of error appear: The first error is introduced by an
Euler--Maruyama discretisation of a Langevin diffusion process, the second
error comes from the data subsampling that enables its use in large-scale data
settings. In this work, we consider an idealised version of SGLD to analyse the
method's pure subsampling error that we then see as a best-case error for
diffusion-based subsampling MCMC methods. Indeed, we introduce and study the
Stochastic Gradient Langevin Diffusion (SGLDiff), a continuous-time Markov
process that follows the Langevin diffusion corresponding to a data subset and
switches this data subset after exponential waiting times. There, we show that
the Wasserstein distance between the posterior and the limiting distribution of
SGLDiff is bounded above by a fractional power of the mean waiting time.
Importantly, this fractional power does not depend on the dimension of the
state space. We bring our results into context with other analyses of SGLD.
|
[
"stat.ML",
"cs.LG",
"stat.CO",
"65C05, 62F15"
] | false |
2305.13883
|
2023-05-23T10:06:22Z
|
On the relevance of APIs facing fairwashed audits
|
[
"Jade Garcia Bourrée",
"Erwan Le Merrer",
"Gilles Tredan",
"Benoît Rottembourg"
] |
Recent legislation required AI platforms to provide APIs for regulators to
assess their compliance with the law. Research has nevertheless shown that
platforms can manipulate their API answers through fairwashing. Facing this
threat for reliable auditing, this paper studies the benefits of the joint use
of platform scraping and of APIs. In this setup, we elaborate on the use of
scraping to detect manipulated answers: since fairwashing only manipulates API
answers, exploiting scraps may reveal a manipulation. To abstract the wide
range of specific API-scrap situations, we introduce a notion of proxy that
captures the consistency an auditor might expect between both data sources. If
the regulator has a good proxy of the consistency, then she can easily detect
manipulation and even bypass the API to conduct her audit. On the other hand,
without a good proxy, relying on the API is necessary, and the auditor cannot
defend against fairwashing.
We then simulate practical scenarios in which the auditor may mostly rely on
the API to conveniently conduct the audit task, while maintaining her chances
to detect a potential manipulation. To highlight the tension between the audit
task and the API fairwashing detection task, we identify Pareto-optimal
strategies in a practical audit scenario.
We believe this research sets the stage for reliable audits in practical and
manipulation-prone setups.
|
[
"cs.LG",
"cs.CY",
"cs.SE"
] | false |
2305.13904
|
2023-05-23T10:26:50Z
|
Deep GEM-Based Network for Weakly Supervised UWB Ranging Error
Mitigation
|
[
"Yuxiao Li",
"Santiago Mazuelas",
"Yuan Shen"
] |
Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches
for high-accurate positioning, tend to be challenged by ranging bias in harsh
environments. The emerging learning-based methods for error mitigation have
shown great performance improvement via exploiting high semantic features from
raw data. However, these methods rely heavily on fully labeled data, leading to
a high cost for data acquisition. We present a learning framework based on weak
supervision for UWB ranging error mitigation. Specifically, we propose a deep
learning method based on the generalized expectation-maximization (GEM)
algorithm for robust UWB ranging error mitigation under weak supervision. Such
method integrate probabilistic modeling into the deep learning scheme, and
adopt weakly supervised labels as prior information. Extensive experiments in
various supervision scenarios illustrate the superiority of the proposed
method.
|
[
"cs.LG",
"cs.IT",
"math.IT",
"stat.AP"
] | false |
2305.13979
|
2023-05-23T12:02:36Z
|
Control of a simulated MRI scanner with deep reinforcement learning
|
[
"Simon Walker-Samuel"
] |
Magnetic resonance imaging (MRI) is a highly versatile and widely used
clinical imaging tool. The content of MRI images is controlled by an
acquisition sequence, which coordinates the timing and magnitude of the scanner
hardware activations, which shape and coordinate the magnetisation within the
body, allowing a coherent signal to be produced. The use of deep reinforcement
learning (DRL) to control this process, and determine new and efficient
acquisition strategies in MRI, has not been explored. Here, we take a first
step into this area, by using DRL to control a virtual MRI scanner, and framing
the problem as a game that aims to efficiently reconstruct the shape of an
imaging phantom using partially reconstructed magnitude images. Our findings
demonstrate that DRL successfully completed two key tasks: inducing the virtual
MRI scanner to generate useful signals and interpreting those signals to
determine the phantom's shape. This proof-of-concept study highlights the
potential of DRL in autonomous MRI data acquisition, shedding light on the
suitability of DRL for complex tasks, with limited supervision, and without the
need to provide human-readable outputs.
|
[
"cs.LG",
"eess.IV",
"physics.bio-ph"
] | false |
2305.14223
|
2023-05-23T16:37:21Z
|
Co-Learning Empirical Games and World Models
|
[
"Max Olan Smith",
"Michael P. Wellman"
] |
Game-based decision-making involves reasoning over both world dynamics and
strategic interactions among the agents. Typically, empirical models capturing
these respective aspects are learned and used separately. We investigate the
potential gain from co-learning these elements: a world model for dynamics and
an empirical game for strategic interactions. Empirical games drive world
models toward a broader consideration of possible game dynamics induced by a
diversity of strategy profiles. Conversely, world models guide empirical games
to efficiently discover new strategies through planning. We demonstrate these
benefits first independently, then in combination as realized by a new
algorithm, Dyna-PSRO, that co-learns an empirical game and a world model. When
compared to PSRO -- a baseline empirical-game building algorithm, Dyna-PSRO is
found to compute lower regret solutions on partially observable general-sum
games. In our experiments, Dyna-PSRO also requires substantially fewer
experiences than PSRO, a key algorithmic advantage for settings where
collecting player-game interaction data is a cost-limiting factor.
|
[
"cs.MA",
"cs.AI",
"cs.GT",
"cs.LG"
] | false |
2305.14311
|
2023-05-23T17:49:56Z
|
Statistical Indistinguishability of Learning Algorithms
|
[
"Alkis Kalavasis",
"Amin Karbasi",
"Shay Moran",
"Grigoris Velegkas"
] |
When two different parties use the same learning rule on their own data, how
can we test whether the distributions of the two outcomes are similar? In this
paper, we study the similarity of outcomes of learning rules through the lens
of the Total Variation (TV) distance of distributions. We say that a learning
rule is TV indistinguishable if the expected TV distance between the posterior
distributions of its outputs, executed on two training data sets drawn
independently from the same distribution, is small. We first investigate the
learnability of hypothesis classes using TV indistinguishable learners. Our
main results are information-theoretic equivalences between TV
indistinguishability and existing algorithmic stability notions such as
replicability and approximate differential privacy. Then, we provide
statistical amplification and boosting algorithms for TV indistinguishable
learners.
|
[
"cs.LG",
"cs.DS",
"stat.ML"
] | false |
2305.14394
|
2023-05-23T05:59:54Z
|
Unsupervised Spiking Neural Network Model of Prefrontal Cortex to study
Task Switching with Synaptic deficiency
|
[
"Ashwin Viswanathan Kannan",
"Goutam Mylavarapu",
"Johnson P Thomas"
] |
In this study, we build a computational model of Prefrontal Cortex (PFC)
using Spiking Neural Networks (SNN) to understand how neurons adapt and respond
to tasks switched under short and longer duration of stimulus changes. We also
explore behavioral deficits arising out of the PFC lesions by simulating
lesioned states in our Spiking architecture model. Although there are some
computational models of the PFC, SNN's have not been used to model them. In
this study, we use SNN's having parameters close to biologically plausible
values and train the model using unsupervised Spike Timing Dependent Plasticity
(STDP) learning rule. Our model is based on connectionist architectures and
exhibits neural phenomena like sustained activity which helps in generating
short-term or working memory. We use these features to simulate lesions by
deactivating synaptic pathways and record the weight adjustments of learned
patterns and capture the accuracy of learning tasks in such conditions. All our
experiments are trained and recorded using a real-world Fashion MNIST (FMNIST)
dataset and through this work, we bridge the gap between bio-realistic models
and those that perform well in pattern recognition tasks
|
[
"cs.NE",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | false |
2305.14396
|
2023-05-23T06:24:43Z
|
FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine
Learning Software
|
[
"Ying Xiao",
"Shangwen Wang",
"Sicen Liu",
"Dingyuan Xue",
"Xian Zhan",
"Yepang Liu"
] |
Software built on top of machine learning algorithms is becoming increasingly
prevalent in a variety of fields, including college admissions, healthcare,
insurance, and justice. The effectiveness and efficiency of these systems
heavily depend on the quality of the training datasets. Biased datasets can
lead to unfair and potentially harmful outcomes, particularly in such critical
decision-making systems where the allocation of resources may be affected. This
can exacerbate discrimination against certain groups and cause significant
social disruption. To mitigate such unfairness, a series of bias-mitigating
methods are proposed. Generally, these studies improve the fairness of the
trained models to a certain degree but with the expense of sacrificing the
model performance. In this paper, we propose FITNESS, a bias mitigation
approach via de-correlating the causal effects between sensitive features
(e.g., the sex) and the label. Our key idea is that by de-correlating such
effects from a causality perspective, the model would avoid making predictions
based on sensitive features and thus fairness could be improved. Furthermore,
FITNESS leverages multi-objective optimization to achieve a better
performance-fairness trade-off. To evaluate the effectiveness, we compare
FITNESS with 7 state-of-the-art methods in 8 benchmark tasks by multiple
metrics. Results show that FITNESS can outperform the state-of-the-art methods
on bias mitigation while preserve the model's performance: it improved the
model's fairness under all the scenarios while decreased the model's
performance under only 26.67% of the scenarios. Additionally, FITNESS surpasses
the Fairea Baseline in 96.72% cases, outperforming all methods we compared.
|
[
"cs.LG",
"cs.CY",
"cs.SE"
] | false |
2305.14397
|
2023-05-23T06:32:49Z
|
Reviewing Evolution of Learning Functions and Semantic Information
Measures for Understanding Deep Learning
|
[
"Chenguang Lu"
] |
A new trend in deep learning, represented by Mutual Information Neural
Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is
emerging. In this trend, similarity functions and Estimated Mutual Information
(EMI) are used as learning and objective functions. Coincidentally, EMI is
essentially the same as Semantic Mutual Information (SeMI) proposed by the
author 30 years ago. This paper first reviews the evolutionary histories of
semantic information measures and learning functions. Then, it briefly
introduces the author's semantic information G theory with the rate-fidelity
function R(G) (G denotes SeMI, and R(G) extends R(D)) and its applications to
multi-label learning, the maximum Mutual Information (MI) classification, and
mixture models. Then it discusses how we should understand the relationship
between SeMI and Shan-non's MI, two generalized entropies (fuzzy entropy and
coverage entropy), Autoencoders, Gibbs distributions, and partition functions
from the perspective of the R(G) function or the G theory. An important
conclusion is that mixture models and Restricted Boltzmann Machines converge
because SeMI is maximized, and Shannon's MI is minimized, making information
efficiency G/R close to 1. A potential opportunity is to simplify deep learning
by using Gaussian channel mixture models for pre-training deep neural networks'
latent layers without considering gradients. It also discusses how the SeMI
measure is used as the reward function (reflecting purposiveness) for
reinforcement learning. The G theory helps interpret deep learning but is far
from enough. Combining semantic information theory and deep learning will
accelerate their development.
|
[
"cs.IT",
"cs.LG",
"math.IT",
"68P30, 94A29, 94A34, 94A15, 94A17, 62B10, 68T05, 62F15, 68P30, 92B20",
"H.1.1; I.1.2; I.2.4; I.2.6; I.5.3; G.3; E.4"
] | false |
2305.14404
|
2023-05-23T11:19:02Z
|
Brain Structure-Function Fusing Representation Learning using
Adversarial Decomposed-VAE for Analyzing MCI
|
[
"Qiankun Zuo",
"Baiying Lei",
"Ning Zhong",
"Yi Pan",
"Shuqiang Wang"
] |
Integrating the brain structural and functional connectivity features is of
great significance in both exploring brain science and analyzing cognitive
impairment clinically. However, it remains a challenge to effectively fuse
structural and functional features in exploring the brain network. In this
paper, a novel brain structure-function fusing-representation learning (BSFL)
model is proposed to effectively learn fused representation from diffusion
tensor imaging (DTI) and resting-state functional magnetic resonance imaging
(fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the
decomposition-fusion framework is developed to first decompose the feature
space into the union of the uniform and the unique spaces for each modality,
and then adaptively fuse the decomposed features to learn MCI-related
representation. Moreover, a knowledge-aware transformer module is designed to
automatically capture local and global connectivity features throughout the
brain. Also, a uniform-unique contrastive loss is further devised to make the
decomposition more effective and enhance the complementarity of structural and
functional features. The extensive experiments demonstrate that the proposed
model achieves better performance than other competitive methods in predicting
and analyzing MCI. More importantly, the proposed model could be a potential
tool for reconstructing unified brain networks and predicting abnormal
connections during the degenerative processes in MCI.
|
[
"q-bio.NC",
"cs.AI",
"cs.LG",
"eess.IV"
] | false |
2305.14451
|
2023-05-23T18:17:49Z
|
Kernel Interpolation with Sparse Grids
|
[
"Mohit Yadav",
"Daniel Sheldon",
"Cameron Musco"
] |
Structured kernel interpolation (SKI) accelerates Gaussian process (GP)
inference by interpolating the kernel covariance function using a dense grid of
inducing points, whose corresponding kernel matrix is highly structured and
thus amenable to fast linear algebra. Unfortunately, SKI scales poorly in the
dimension of the input points, since the dense grid size grows exponentially
with the dimension. To mitigate this issue, we propose the use of sparse grids
within the SKI framework. These grids enable accurate interpolation, but with a
number of points growing more slowly with dimension. We contribute a novel
nearly linear time matrix-vector multiplication algorithm for the sparse grid
kernel matrix. Next, we describe how sparse grids can be combined with an
efficient interpolation scheme based on simplices. With these changes, we
demonstrate that SKI can be scaled to higher dimensions while maintaining
accuracy.
|
[
"cs.LG",
"cs.AI",
"stat.ML"
] | false |
2305.14485
|
2023-05-23T19:32:42Z
|
Knowledge Graphs Querying
|
[
"Arijit Khan"
] |
Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL
were constructed to store large-scale, real-world facts as (subject, predicate,
object) triples -- that can also be modeled as a graph, where a node (a subject
or an object) represents an entity with attributes, and a directed edge (a
predicate) is a relationship between two entities. Querying KGs is critical in
web search, question answering (QA), semantic search, personal assistants, fact
checking, and recommendation. While significant progress has been made on KG
construction and curation, thanks to deep learning recently we have seen a
surge of research on KG querying and QA. The objectives of our survey are
two-fold. First, research on KG querying has been conducted by several
communities, such as databases, data mining, semantic web, machine learning,
information retrieval, and natural language processing (NLP), with different
focus and terminologies; and also in diverse topics ranging from graph
databases, query languages, join algorithms, graph patterns matching, to more
sophisticated KG embedding and natural language questions (NLQs). We aim at
uniting different interdisciplinary topics and concepts that have been
developed for KG querying. Second, many recent advances on KG and query
embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and
computer vision domains. We identify important challenges of KG querying that
received less attention by graph databases, and by the DB community in general,
e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude
by discussing interesting opportunities for the data management community, for
instance, KG as a unified data model and vector-based query processing.
|
[
"cs.DB",
"cs.IR",
"cs.LG"
] | false |
2305.14537
|
2023-05-23T21:47:31Z
|
Disincentivizing Polarization in Social Networks
|
[
"Christian Borgs",
"Jennifer Chayes",
"Christian Ikeokwu",
"Ellen Vitercik"
] |
On social networks, algorithmic personalization drives users into filter
bubbles where they rarely see content that deviates from their interests. We
present a model for content curation and personalization that avoids filter
bubbles, along with algorithmic guarantees and nearly matching lower bounds. In
our model, the platform interacts with $n$ users over $T$ timesteps, choosing
content for each user from $k$ categories. The platform receives stochastic
rewards as in a multi-arm bandit. To avoid filter bubbles, we draw on the
intuition that if some users are shown some category of content, then all users
should see at least a small amount of that content. We first analyze a naive
formalization of this intuition and show it has unintended consequences: it
leads to ``tyranny of the majority'' with the burden of diversification borne
disproportionately by those with minority interests. This leads us to our model
which distributes this burden more equitably. We require that the probability
any user is shown a particular type of content is at least $\gamma$ times the
average probability all users are shown that type of content. Full
personalization corresponds to $\gamma = 0$ and complete homogenization
corresponds to $\gamma = 1$; hence, $\gamma$ encodes a hard cap on the level of
personalization. We also analyze additional formulations where the platform can
exceed its cap but pays a penalty proportional to its constraint violation. We
provide algorithmic guarantees for optimizing recommendations subject to these
constraints. These include nearly matching upper and lower bounds for the
entire range of $\gamma \in [0,1]$ showing that the reward of a multi-agent
variant of UCB is nearly optimal. Using real-world preference data, we
empirically verify that under our model, users share the burden of
diversification with only minor utility loss under our constraints.
|
[
"cs.CY",
"cs.AI",
"cs.LG"
] | false |
2305.14547
|
2023-05-23T22:03:08Z
|
Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural
Network Training
|
[
"Yuting Wu",
"Qiwen Wang",
"Ziyu Wang",
"Xinxin Wang",
"Buvna Ayyagari",
"Siddarth Krishnan",
"Michael Chudzik",
"Wei D. Lu"
] |
The need for deep neural network (DNN) models with higher performance and
better functionality leads to the proliferation of very large models. Model
training, however, requires intensive computation time and energy.
Memristor-based compute-in-memory (CIM) modules can perform vector-matrix
multiplication (VMM) in situ and in parallel, and have shown great promises in
DNN inference applications. However, CIM-based model training faces challenges
due to non-linear weight updates, device variations, and low-precision in
analog computing circuits. In this work, we experimentally implement a
mixed-precision training scheme to mitigate these effects using a
bulk-switching memristor CIM module. Lowprecision CIM modules are used to
accelerate the expensive VMM operations, with high precision weight updates
accumulated in digital units. Memristor devices are only changed when the
accumulated weight update value exceeds a pre-defined threshold. The proposed
scheme is implemented with a system-on-chip (SoC) of fully integrated analog
CIM modules and digital sub-systems, showing fast convergence of LeNet training
to 97.73%. The efficacy of training larger models is evaluated using realistic
hardware parameters and shows that that analog CIM modules can enable efficient
mix-precision DNN training with accuracy comparable to full-precision software
trained models. Additionally, models trained on chip are inherently robust to
hardware variations, allowing direct mapping to CIM inference chips without
additional re-training.
|
[
"cs.AR",
"cs.ET",
"cs.LG"
] | false |
2305.14562
|
2023-05-23T23:02:21Z
|
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous
Computing
|
[
"Yi Hu",
"Chaoran Zhang",
"Edward Andert",
"Harshul Singh",
"Aviral Shrivastava",
"James Laudon",
"Yanqi Zhou",
"Bob Iannucci",
"Carlee Joe-Wong"
] |
Careful placement of a computational application within a target device
cluster is critical for achieving low application completion time. The problem
is challenging due to its NP-hardness and combinatorial nature. In recent
years, learning-based approaches have been proposed to learn a placement policy
that can be applied to unseen applications, motivated by the problem of placing
a neural network across cloud servers. These approaches, however, generally
assume the device cluster is fixed, which is not the case in mobile or edge
computing settings, where heterogeneous devices move in and out of range for a
particular application. We propose a new learning approach called GiPH, which
learns policies that generalize to dynamic device clusters via 1) a novel graph
representation gpNet that efficiently encodes the information needed for
choosing a good placement, and 2) a scalable graph neural network (GNN) that
learns a summary of the gpNet information. GiPH turns the placement problem
into that of finding a sequence of placement improvements, learning a policy
for selecting this sequence that scales to problems of arbitrary size. We
evaluate GiPH with a wide range of task graphs and device clusters and show
that our learned policy rapidly find good placements for new problem instances.
GiPH finds placements with up to 30.5% lower completion times, searching up to
3X faster than other search-based placement policies.
|
[
"cs.LG",
"cs.SY",
"eess.SY"
] | false |
2305.17141
|
2023-05-23T14:20:14Z
|
Research on Multi-Agent Communication and Collaborative Decision-Making
Based on Deep Reinforcement Learning
|
[
"Zeng Da"
] |
In a multi-agent environment, In order to overcome and alleviate the
non-stationarity of the multi-agent environment, the mainstream method is to
adopt the framework of Centralized Training Decentralized Execution (CTDE).
This thesis is based on the framework of CTDE, and studies the cooperative
decision-making of multi-agent based on the Multi-Agent Proximal Policy
Optimization (MAPPO) algorithm for multi-agent proximal policy optimization. In
order to alleviate the non-stationarity of the multi-agent environment, a
multi-agent communication mechanism based on weight scheduling and attention
module is introduced. Different agents can alleviate the non-stationarity
caused by local observations through information exchange between agents,
assisting in the collaborative decision-making of agents. The specific method
is to introduce a communication module in the policy network part. The
communication module is composed of a weight generator, a weight scheduler, a
message encoder, a message pool and an attention module. Among them, the weight
generator and weight scheduler will generate weights as the selection basis for
communication, the message encoder is used to compress and encode communication
information, the message pool is used to store communication messages, and the
attention module realizes the interactive processing of the agent's own
information and communication information. This thesis proposes a Multi-Agent
Communication and Global Information Optimization Proximal Policy
Optimization(MCGOPPO)algorithm, and conducted experiments in the SMAC and the
MPE. The experimental results show that the improvement has achieved certain
effects, which can better alleviate the non-stationarity of the multi-agent
environment, and improve the collaborative decision-making ability among the
agents.
|
[
"cs.MA",
"cs.AI",
"cs.LG"
] | false |
2305.18206
|
2023-05-23T10:16:22Z
|
Deep Generative Model for Simultaneous Range Error Mitigation and
Environment Identification
|
[
"Yuxiao Li",
"Santiago Mazuelas",
"Yuan Shen"
] |
Received waveforms contain rich information for both range information and
environment semantics. However, its full potential is hard to exploit under
multipath and non-line-of-sight conditions. This paper proposes a deep
generative model (DGM) for simultaneous range error mitigation and environment
identification. In particular, we present a Bayesian model for the generative
process of the received waveform composed by latent variables for both
range-related features and environment semantics. The simultaneous range error
mitigation and environment identification is interpreted as an inference
problem based on the DGM, and implemented in a unique end-to-end learning
scheme. Comprehensive experiments on a general Ultra-wideband dataset
demonstrate the superior performance on range error mitigation, scalability to
different environments, and novel capability on simultaneous environment
identification.
|
[
"eess.SP",
"cs.AI",
"cs.LG",
"stat.AP"
] | false |
2305.18208
|
2023-05-23T10:08:42Z
|
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based
on UWB Waveform
|
[
"Yuxiao Li",
"Santiago Mazuelas",
"Yuan Shen"
] |
Localization systems based on ultra-wide band (UWB) measurements can have
unsatisfactory performance in harsh environments due to the presence of
non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation
have shown great performance improvement via directly exploiting the wideband
waveform instead of handcrafted features. However, these methods require data
samples fully labeled with actual measurement errors for training, which leads
to time-consuming data collection. In this paper, we propose a semi-supervised
learning method based on variational Bayes for UWB ranging error mitigation.
Combining deep learning techniques and statistic tools, our method can
efficiently accumulate knowledge from both labeled and unlabeled data samples.
Extensive experiments illustrate the effectiveness of the proposed method under
different supervision rates, and the superiority compared to other fully
supervised methods even at a low supervision rate.
|
[
"eess.SP",
"cs.AI",
"cs.LG",
"stat.AP"
] | false |
2306.01754
|
2023-05-23T01:21:55Z
|
Transformer-based Vulnerability Detection in Code at EditTime:
Zero-shot, Few-shot, or Fine-tuning?
|
[
"Aaron Chan",
"Anant Kharkar",
"Roshanak Zilouchian Moghaddam",
"Yevhen Mohylevskyy",
"Alec Helyar",
"Eslam Kamal",
"Mohamed Elkamhawy",
"Neel Sundaresan"
] |
Software vulnerabilities bear enterprises significant costs. Despite
extensive efforts in research and development of software vulnerability
detection methods, uncaught vulnerabilities continue to put software owners and
users at risk. Many current vulnerability detection methods require that code
snippets can compile and build before attempting detection. This,
unfortunately, introduces a long latency between the time a vulnerability is
injected to the time it is removed, which can substantially increases the cost
of fixing a vulnerability. We recognize that the current advances in machine
learning can be used to detect vulnerable code patterns on syntactically
incomplete code snippets as the developer is writing the code at EditTime. In
this paper we present a practical system that leverages deep learning on a
large-scale data set of vulnerable code patterns to learn complex
manifestations of more than 250 vulnerability types and detect vulnerable code
patterns at EditTime. We discuss zero-shot, few-shot, and fine-tuning
approaches on state of the art pre-trained Large Language Models (LLMs). We
show that in comparison with state of the art vulnerability detection models
our approach improves the state of the art by 10%. We also evaluate our
approach to detect vulnerability in auto-generated code by code LLMs.
Evaluation on a benchmark of high-risk code scenarios shows a reduction of up
to 90% vulnerability reduction.
|
[
"cs.CR",
"cs.AI",
"cs.LG"
] | true |
2305.14080
|
2023-05-23T14:02:38Z
|
Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and
Privacy Challenges
|
[
"Efe Bozkir",
"Süleyman Özdel",
"Mengdi Wang",
"Brendan David-John",
"Hong Gao",
"Kevin Butler",
"Eakta Jain",
"Enkelejda Kasneci"
] |
Latest developments in computer hardware, sensor technologies, and artificial
intelligence can make virtual reality (VR) and virtual spaces an important part
of human everyday life. Eye tracking offers not only a hands-free way of
interaction but also the possibility of a deeper understanding of human visual
attention and cognitive processes in VR. Despite these possibilities,
eye-tracking data also reveal privacy-sensitive attributes of users when it is
combined with the information about the presented stimulus. To address these
possibilities and potential privacy issues, in this survey, we first cover
major works in eye tracking, VR, and privacy areas between the years 2012 and
2022. While eye tracking in the VR part covers the complete pipeline of
eye-tracking methodology from pupil detection and gaze estimation to offline
use and analyses, as for privacy and security, we focus on eye-based
authentication as well as computational methods to preserve the privacy of
individuals and their eye-tracking data in VR. Later, taking all into
consideration, we draw three main directions for the research community by
mainly focusing on privacy challenges. In summary, this survey provides an
extensive literature review of the utmost possibilities with eye tracking in VR
and the privacy implications of those possibilities.
|
[
"cs.HC",
"cs.AI",
"cs.CR",
"cs.GR",
"cs.LG"
] | false |
2305.16402
|
2023-05-23T19:00:09Z
|
Support Vector Machine Guided Reproducing Kernel Particle Method for
Image-Based Modeling of Microstructures
|
[
"Yanran Wang",
"Jonghyuk Baek",
"Yichun Tang",
"Jing Du",
"Mike Hillman",
"J. S. Chen"
] |
This work presents an approach for automating the discretization and
approximation procedures in constructing digital representations of composites
from Micro-CT images featuring intricate microstructures. The proposed method
is guided by the Support Vector Machine (SVM) classification, offering an
effective approach for discretizing microstructural images. An SVM soft margin
training process is introduced as a classification of heterogeneous material
points, and image segmentation is accomplished by identifying support vectors
through a local regularized optimization problem. In addition, an
Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for
appropriate approximations of weak discontinuities across material interfaces.
The proposed method modifies the smooth kernel functions with a regularized
heavy-side function concerning the material interfaces to alleviate Gibb's
oscillations. This IM-RKPM is formulated without introducing duplicated degrees
of freedom associated with the interface nodes commonly needed in the
conventional treatments of weak discontinuities in the meshfree methods.
Moreover, IM-RKPM can be implemented with various domain integration
techniques, such as Stabilized Conforming Nodal Integration (SCNI). The
extension of the proposed method to 3-dimension is straightforward, and the
effectiveness of the proposed method is validated through the image-based
modeling of polymer-ceramic composite microstructures.
|
[
"cs.LG",
"cs.CE",
"cs.NA",
"math.NA",
"physics.app-ph"
] | false |
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