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2305.11271
|
2023-05-18T19:42:04Z
|
Towards Collaborative Plan Acquisition through Theory of Mind Modeling
in Situated Dialogue
|
[
"Cristian-Paul Bara",
"Ziqiao Ma",
"Yingzhuo Yu",
"Julie Shah",
"Joyce Chai"
] |
Collaborative tasks often begin with partial task knowledge and incomplete
initial plans from each partner. To complete these tasks, agents need to engage
in situated communication with their partners and coordinate their partial
plans towards a complete plan to achieve a joint task goal. While such
collaboration seems effortless in a human-human team, it is highly challenging
for human-AI collaboration. To address this limitation, this paper takes a step
towards collaborative plan acquisition, where humans and agents strive to learn
and communicate with each other to acquire a complete plan for joint tasks.
Specifically, we formulate a novel problem for agents to predict the missing
task knowledge for themselves and for their partners based on rich perceptual
and dialogue history. We extend a situated dialogue benchmark for symmetric
collaborative tasks in a 3D blocks world and investigate computational
strategies for plan acquisition. Our empirical results suggest that predicting
the partner's missing knowledge is a more viable approach than predicting one's
own. We show that explicit modeling of the partner's dialogue moves and mental
states produces improved and more stable results than without. These results
provide insight for future AI agents that can predict what knowledge their
partner is missing and, therefore, can proactively communicate such information
to help their partner acquire such missing knowledge toward a common
understanding of joint tasks.
|
[
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | false |
2305.11327
|
2023-05-18T22:25:50Z
|
MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval
|
[
"Bhanu Prakash Voutharoja",
"Peng Wang",
"Lei Wang",
"Vivienne Guan"
] |
Image-to-recipe retrieval is a challenging vision-to-language task of
significant practical value. The main challenge of the task lies in the
ultra-high redundancy in the long recipe and the large variation reflected in
both food item combination and food item appearance. A de-facto idea to address
this task is to learn a shared feature embedding space in which a food image is
aligned better to its paired recipe than other recipes. However, such
supervised global matching is prone to supervision collapse, i.e., only partial
information that is necessary for distinguishing training pairs can be
identified, while other information that is potentially useful in
generalization could be lost. To mitigate such a problem, we propose a
mask-augmentation-based local matching network (MALM), where an image-text
matching module and a masked self-distillation module benefit each other
mutually to learn generalizable cross-modality representations. On one hand, we
perform local matching between the tokenized representations of image and text
to locate fine-grained cross-modality correspondence explicitly. We involve
representations of masked image patches in this process to alleviate
overfitting resulting from local matching especially when some food items are
underrepresented. On the other hand, predicting the hidden representations of
the masked patches through self-distillation helps to learn general-purpose
image representations that are expected to generalize better. And the
multi-task nature of the model enables the representations of masked patches to
be text-aware and thus facilitates the lost information reconstruction.
Experimental results on Recipe1M dataset show our method can clearly outperform
state-of-the-art (SOTA) methods. Our code will be available at
https://github.com/MyFoodChoice/MALM_Mask_Augmentation_based_Local_Matching-_for-_Food_Recipe_Retrieval
|
[
"cs.CV",
"cs.LG",
"cs.MM"
] | false |
2305.11347
|
2023-05-18T23:43:33Z
|
Quantifying the robustness of deep multispectral segmentation models
against natural perturbations and data poisoning
|
[
"Elise Bishoff",
"Charles Godfrey",
"Myles McKay",
"Eleanor Byler"
] |
In overhead image segmentation tasks, including additional spectral bands
beyond the traditional RGB channels can improve model performance. However, it
is still unclear how incorporating this additional data impacts model
robustness to adversarial attacks and natural perturbations. For adversarial
robustness, the additional information could improve the model's ability to
distinguish malicious inputs, or simply provide new attack avenues and
vulnerabilities. For natural perturbations, the additional information could
better inform model decisions and weaken perturbation effects or have no
significant influence at all. In this work, we seek to characterize the
performance and robustness of a multispectral (RGB and near infrared) image
segmentation model subjected to adversarial attacks and natural perturbations.
While existing adversarial and natural robustness research has focused
primarily on digital perturbations, we prioritize on creating realistic
perturbations designed with physical world conditions in mind. For adversarial
robustness, we focus on data poisoning attacks whereas for natural robustness,
we focus on extending ImageNet-C common corruptions for fog and snow that
coherently and self-consistently perturbs the input data. Overall, we find both
RGB and multispectral models are vulnerable to data poisoning attacks
regardless of input or fusion architectures and that while physically
realizable natural perturbations still degrade model performance, the impact
differs based on fusion architecture and input data.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.11094
|
2023-05-18T16:31:25Z
|
QPGesture: Quantization-Based and Phase-Guided Motion Matching for
Natural Speech-Driven Gesture Generation
|
[
"Sicheng Yang",
"Zhiyong Wu",
"Minglei Li",
"Zhensong Zhang",
"Lei Hao",
"Weihong Bao",
"Haolin Zhuang"
] |
Speech-driven gesture generation is highly challenging due to the random
jitters of human motion. In addition, there is an inherent asynchronous
relationship between human speech and gestures. To tackle these challenges, we
introduce a novel quantization-based and phase-guided motion-matching
framework. Specifically, we first present a gesture VQ-VAE module to learn a
codebook to summarize meaningful gesture units. With each code representing a
unique gesture, random jittering problems are alleviated effectively. We then
use Levenshtein distance to align diverse gestures with different speech.
Levenshtein distance based on audio quantization as a similarity metric of
corresponding speech of gestures helps match more appropriate gestures with
speech, and solves the alignment problem of speech and gestures well. Moreover,
we introduce phase to guide the optimal gesture matching based on the semantics
of context or rhythm of audio. Phase guides when text-based or speech-based
gestures should be performed to make the generated gestures more natural.
Extensive experiments show that our method outperforms recent approaches on
speech-driven gesture generation. Our code, database, pre-trained models, and
demos are available at https://github.com/YoungSeng/QPGesture.
|
[
"cs.HC",
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | false |
2305.10845
|
2023-05-18T09:58:19Z
|
TAPIR: Learning Adaptive Revision for Incremental Natural Language
Understanding with a Two-Pass Model
|
[
"Patrick Kahardipraja",
"Brielen Madureira",
"David Schlangen"
] |
Language is by its very nature incremental in how it is produced and
processed. This property can be exploited by NLP systems to produce fast
responses, which has been shown to be beneficial for real-time interactive
applications. Recent neural network-based approaches for incremental processing
mainly use RNNs or Transformers. RNNs are fast but monotonic (cannot correct
earlier output, which can be necessary in incremental processing).
Transformers, on the other hand, consume whole sequences, and hence are by
nature non-incremental. A restart-incremental interface that repeatedly passes
longer input prefixes can be used to obtain partial outputs, while providing
the ability to revise. However, this method becomes costly as the sentence
grows longer. In this work, we propose the Two-pass model for AdaPtIve Revision
(TAPIR) and introduce a method to obtain an incremental supervision signal for
learning an adaptive revision policy. Experimental results on sequence
labelling show that our model has better incremental performance and faster
inference speed compared to restart-incremental Transformers, while showing
little degradation on full sequences.
|
[
"cs.CL"
] | false |
2305.10848
|
2023-05-18T10:07:50Z
|
Advancing Full-Text Search Lemmatization Techniques with Paradigm
Retrieval from OpenCorpora
|
[
"Dmitriy Kalugin-Balashov"
] |
In this paper, we unveil a groundbreaking method to amplify full-text search
lemmatization, utilizing the OpenCorpora dataset and a bespoke paradigm
retrieval algorithm. Our primary aim is to streamline the extraction of a
word's primary form or lemma - a crucial factor in full-text search.
Additionally, we propose a compact dictionary storage strategy, significantly
boosting the speed and precision of lemma retrieval.
|
[
"cs.CL"
] | false |
2305.10866
|
2023-05-18T10:38:06Z
|
TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse
Relation Recognition
|
[
"Wei Xiang",
"Chao Liang",
"Bang Wang"
] |
Implicit Discourse Relation Recognition (IDRR) aims at classifying the
relation sense between two arguments without an explicit connective. Recently,
the ConnPrompt~\cite{Wei.X:et.al:2022:COLING} has leveraged the powerful prompt
learning for IDRR based on the fusion of multi-prompt decisions from three
different yet much similar connective prediction templates. Instead of
multi-prompt ensembling, we propose to design auxiliary tasks with enlightened
prompt learning for the IDRR task. Although an auxiliary task is not used to
directly output final prediction, we argue that during the joint training some
of its learned features can be useful to boost the main task. In light of such
motivations, we propose a task enlightenment prompt learning model, called
TEPrompt, to fuse learned features from three related tasks for IDRR. In
particular, the TEPrompt contains three tasks, viz., Discourse Relation
Recognition (DRR), Sense Semantics Classification (SSC) and Annotated
Connective Prediction (ACP), each with a unique prompt template and an answer
space. In the training phase, we jointly train three prompt learning tasks with
shared argument representation. In the testing phase, we only take the DRR
output with fused features as the final IDRR decision. Experiments with the
same conditions have shown that the proposed TEPrompt outperforms the
ConnPrompt. This can be attributed to the promoted decision features and
language models benefited from joint-training of auxiliary tasks.
|
[
"cs.CL"
] | false |
2305.10907
|
2023-05-18T12:10:06Z
|
Take a Break in the Middle: Investigating Subgoals towards Hierarchical
Script Generation
|
[
"Xinze Li",
"Yixin Cao",
"Muhao Chen",
"Aixin Sun"
] |
Goal-oriented Script Generation is a new task of generating a list of steps
that can fulfill the given goal. In this paper, we propose to extend the task
from the perspective of cognitive theory. Instead of a simple flat structure,
the steps are typically organized hierarchically - Human often decompose a
complex task into subgoals, where each subgoal can be further decomposed into
steps. To establish the benchmark, we contribute a new dataset, propose several
baseline methods, and set up evaluation metrics. Both automatic and human
evaluation verify the high-quality of dataset, as well as the effectiveness of
incorporating subgoals into hierarchical script generation. Furthermore, We
also design and evaluate the model to discover subgoal, and find that it is a
bit more difficult to decompose the goals than summarizing from segmented
steps.
|
[
"cs.CL"
] | false |
2305.10985
|
2023-05-18T14:01:33Z
|
Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation
Extraction
|
[
"Elisa Bassignana",
"Filip Ginter",
"Sampo Pyysalo",
"Rob van der Goot",
"Barbara Plank"
] |
Most research in Relation Extraction (RE) involves the English language,
mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE,
the broadest multi-lingual dataset for RE, including 26 languages in addition
to English, and covering six text domains. Multi-CrossRE is a machine
translated version of CrossRE (Bassignana and Plank, 2022), with a sub-portion
including more than 200 sentences in seven diverse languages checked by native
speakers. We run a baseline model over the 26 new datasets and--as sanity
check--over the 26 back-translations to English. Results on the back-translated
data are consistent with the ones on the original English CrossRE, indicating
high quality of the translation and the resulting dataset.
|
[
"cs.CL"
] | false |
2305.11016
|
2023-05-18T14:49:19Z
|
Silver Syntax Pre-training for Cross-Domain Relation Extraction
|
[
"Elisa Bassignana",
"Filip Ginter",
"Sampo Pyysalo",
"Rob van der Goot",
"Barbara Plank"
] |
Relation Extraction (RE) remains a challenging task, especially when
considering realistic out-of-domain evaluations. One of the main reasons for
this is the limited training size of current RE datasets: obtaining
high-quality (manually annotated) data is extremely expensive and cannot
realistically be repeated for each new domain. An intermediate training step on
data from related tasks has shown to be beneficial across many NLP
tasks.However, this setup still requires supplementary annotated data, which is
often not available. In this paper, we investigate intermediate pre-training
specifically for RE. We exploit the affinity between syntactic structure and
semantic RE, and identify the syntactic relations which are closely related to
RE by being on the shortest dependency path between two entities. We then take
advantage of the high accuracy of current syntactic parsers in order to
automatically obtain large amounts of low-cost pre-training data. By
pre-training our RE model on the relevant syntactic relations, we are able to
outperform the baseline in five out of six cross-domain setups, without any
additional annotated data.
|
[
"cs.CL"
] | false |
2305.11023
|
2023-05-18T15:04:52Z
|
Generalized Multiple Intent Conditioned Slot Filling
|
[
"Harshil Shah",
"Arthur Wilcke",
"Marius Cobzarenco",
"Cristi Cobzarenco",
"Edward Challis",
"David Barber"
] |
Natural language understanding includes the tasks of intent detection
(identifying a user's objectives) and slot filling (extracting the entities
relevant to those objectives). Prior slot filling methods assume that each
intent type cannot occur more than once within a message, however this is often
not a valid assumption for real-world settings. In this work, we generalize
slot filling by removing the constraint of unique intents in a message. We cast
this as a JSON generation task and approach it using a language model. We
create a pre-training dataset by combining DBpedia and existing slot filling
datasets that we convert for JSON generation. We also generate an in-domain
dataset using GPT-3. We train T5 models for this task (with and without
exemplars in the prompt) and find that both training datasets improve
performance, and that the model is able to generalize to intent types not seen
during training.
|
[
"cs.CL"
] | false |
2305.11034
|
2023-05-18T15:22:00Z
|
Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words
Extraction with Wordpieces and Aspect Enhancement
|
[
"Samuel Mensah",
"Kai Sun",
"Nikolaos Aletras"
] |
State-of-the-art target-oriented opinion word extraction (TOWE) models
typically use BERT-based text encoders that operate on the word level, along
with graph convolutional networks (GCNs) that incorporate syntactic information
extracted from syntax trees. These methods achieve limited gains with GCNs and
have difficulty using BERT wordpieces. Meanwhile, BERT wordpieces are known to
be effective at representing rare words or words with insufficient context
information. To address this issue, this work trades syntax trees for BERT
wordpieces by entirely removing the GCN component from the methods'
architectures. To enhance TOWE performance, we tackle the issue of aspect
representation loss during encoding. Instead of solely utilizing a sentence as
the input, we use a sentence-aspect pair. Our relatively simple approach
achieves state-of-the-art results on benchmark datasets and should serve as a
strong baseline for further research.
|
[
"cs.CL"
] | false |
2305.11140
|
2023-05-18T17:35:28Z
|
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model
|
[
"Chantal Amrhein",
"Florian Schottmann",
"Rico Sennrich",
"Samuel Läubli"
] |
Natural language generation models reproduce and often amplify the biases
present in their training data. Previous research explored using
sequence-to-sequence rewriting models to transform biased model outputs (or
original texts) into more gender-fair language by creating pseudo training data
through linguistic rules. However, this approach is not practical for languages
with more complex morphology than English. We hypothesise that creating
training data in the reverse direction, i.e. starting from gender-fair text, is
easier for morphologically complex languages and show that it matches the
performance of state-of-the-art rewriting models for English. To eliminate the
rule-based nature of data creation, we instead propose using machine
translation models to create gender-biased text from real gender-fair text via
round-trip translation. Our approach allows us to train a rewriting model for
German without the need for elaborate handcrafted rules. The outputs of this
model increased gender-fairness as shown in a human evaluation study.
|
[
"cs.CL",
"I.2.7"
] | false |
2305.11142
|
2023-05-18T17:36:41Z
|
Discourse Centric Evaluation of Machine Translation with a Densely
Annotated Parallel Corpus
|
[
"Yuchen Eleanor Jiang",
"Tianyu Liu",
"Shuming Ma",
"Dongdong Zhang",
"Mrinmaya Sachan",
"Ryan Cotterell"
] |
Several recent papers claim human parity at sentence-level Machine
Translation (MT), especially in high-resource languages. Thus, in response, the
MT community has, in part, shifted its focus to document-level translation.
Translating documents requires a deeper understanding of the structure and
meaning of text, which is often captured by various kinds of discourse
phenomena such as consistency, coherence, and cohesion. However, this renders
conventional sentence-level MT evaluation benchmarks inadequate for evaluating
the performance of context-aware MT systems. This paper presents a new dataset
with rich discourse annotations, built upon the large-scale parallel corpus BWB
introduced in Jiang et al. (2022). The new BWB annotation introduces four extra
evaluation aspects, i.e., entity, terminology, coreference, and quotation,
covering 15,095 entity mentions in both languages. Using these annotations, we
systematically investigate the similarities and differences between the
discourse structures of source and target languages, and the challenges they
pose to MT. We discover that MT outputs differ fundamentally from human
translations in terms of their latent discourse structures. This gives us a new
perspective on the challenges and opportunities in document-level MT. We make
our resource publicly available to spur future research in document-level MT
and the generalization to other language translation tasks.
|
[
"cs.CL"
] | false |
2305.11159
|
2023-05-18T17:48:03Z
|
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot
Relation Extractors
|
[
"Kai Zhang",
"Bernal Jiménez Gutiérrez",
"Yu Su"
] |
Recent work has shown that fine-tuning large language models (LLMs) on
large-scale instruction-following datasets substantially improves their
performance on a wide range of NLP tasks, especially in the zero-shot setting.
However, even advanced instruction-tuned LLMs still fail to outperform small
LMs on relation extraction (RE), a fundamental information extraction task. We
hypothesize that instruction-tuning has been unable to elicit strong RE
capabilities in LLMs due to RE's low incidence in instruction-tuning datasets,
making up less than 1% of all tasks (Wang et al., 2022). To address this
limitation, we propose QA4RE, a framework that aligns RE with question
answering (QA), a predominant task in instruction-tuning datasets.
Comprehensive zero-shot RE experiments over four datasets with two series of
instruction-tuned LLMs (six LLMs in total) demonstrate that our QA4RE framework
consistently improves LLM performance, strongly verifying our hypothesis and
enabling LLMs to outperform strong zero-shot baselines by a large margin.
Additionally, we provide thorough experiments and discussions to show the
robustness, few-shot effectiveness, and strong transferability of our QA4RE
framework. This work illustrates a promising way of adapting LLMs to
challenging and underrepresented tasks by aligning these tasks with more common
instruction-tuning tasks like QA.
|
[
"cs.CL"
] | false |
2305.11231
|
2023-05-18T18:00:44Z
|
Recent Trends in Unsupervised Summarization
|
[
"Mohammad Khosravani",
"Amine Trabelsi"
] |
Unsupervised summarization is a powerful technique that enables training
summarizing models without requiring labeled datasets. This survey covers
different recent techniques and models used for unsupervised summarization. We
cover extractive, abstractive, and hybrid models and strategies used to achieve
unsupervised summarization. While the main focus of this survey is on recent
research, we also cover some of the important previous research. We
additionally introduce a taxonomy, classifying different research based on
their approach to unsupervised training. Finally, we discuss the current
approaches and mention some datasets and evaluation methods.
|
[
"cs.CL"
] | false |
2305.11242
|
2023-05-18T18:15:07Z
|
Comparing Biases and the Impact of Multilingual Training across Multiple
Languages
|
[
"Sharon Levy",
"Neha Anna John",
"Ling Liu",
"Yogarshi Vyas",
"Jie Ma",
"Yoshinari Fujinuma",
"Miguel Ballesteros",
"Vittorio Castelli",
"Dan Roth"
] |
Studies in bias and fairness in natural language processing have primarily
examined social biases within a single language and/or across few attributes
(e.g. gender, race). However, biases can manifest differently across various
languages for individual attributes. As a result, it is critical to examine
biases within each language and attribute. Of equal importance is to study how
these biases compare across languages and how the biases are affected when
training a model on multilingual data versus monolingual data. We present a
bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the
downstream sentiment analysis task to observe whether specific demographics are
viewed more positively. We study bias similarities and differences across these
languages and investigate the impact of multilingual vs. monolingual training
data. We adapt existing sentiment bias templates in English to Italian,
Chinese, Hebrew, and Spanish for four attributes: race, religion, nationality,
and gender. Our results reveal similarities in bias expression such as
favoritism of groups that are dominant in each language's culture (e.g.
majority religions and nationalities). Additionally, we find an increased
variation in predictions across protected groups, indicating bias
amplification, after multilingual finetuning in comparison to multilingual
pretraining.
|
[
"cs.CL"
] | false |
2305.11251
|
2023-05-18T18:32:03Z
|
Computational thematics: Comparing algorithms for clustering the genres
of literary fiction
|
[
"Oleg Sobchuk",
"Artjoms Šeļa"
] |
What are the best methods of capturing thematic similarity between literary
texts? Knowing the answer to this question would be useful for automatic
clustering of book genres, or any other thematic grouping. This paper compares
a variety of algorithms for unsupervised learning of thematic similarities
between texts, which we call "computational thematics". These algorithms belong
to three steps of analysis: text preprocessing, extraction of text features,
and measuring distances between the lists of features. Each of these steps
includes a variety of options. We test all the possible combinations of these
options: every combination of algorithms is given a task to cluster a corpus of
books belonging to four pre-tagged genres of fiction. This clustering is then
validated against the "ground truth" genre labels. Such comparison of
algorithms allows us to learn the best and the worst combinations for
computational thematic analysis. To illustrate the sharp difference between the
best and the worst methods, we then cluster 5000 random novels from the
HathiTrust corpus of fiction.
|
[
"cs.CL"
] | false |
2305.11262
|
2023-05-18T18:58:30Z
|
CHBias: Bias Evaluation and Mitigation of Chinese Conversational
Language Models
|
[
"Jiaxu Zhao",
"Meng Fang",
"Zijing Shi",
"Yitong Li",
"Ling Chen",
"Mykola Pechenizkiy"
] |
\textit{\textbf{\textcolor{red}{Warning}:} This paper contains content that
may be offensive or upsetting.} Pretrained conversational agents have been
exposed to safety issues, exhibiting a range of stereotypical human biases such
as gender bias. However, there are still limited bias categories in current
research, and most of them only focus on English. In this paper, we introduce a
new Chinese dataset, CHBias, for bias evaluation and mitigation of Chinese
conversational language models. Apart from those previous well-explored bias
categories, CHBias includes under-explored bias categories, such as ageism and
appearance biases, which received less attention. We evaluate two popular
pretrained Chinese conversational models, CDial-GPT and EVA2.0, using CHBias.
Furthermore, to mitigate different biases, we apply several debiasing methods
to the Chinese pretrained models. Experimental results show that these Chinese
pretrained models are potentially risky for generating texts that contain
social biases, and debiasing methods using the proposed dataset can make
response generation less biased while preserving the models' conversational
capabilities.
|
[
"cs.CL"
] | false |
2305.11315
|
2023-05-18T21:52:48Z
|
Improving Toponym Resolution with Better Candidate Generation,
Transformer-based Reranking, and Two-Stage Resolution
|
[
"Zeyu Zhang",
"Steven Bethard"
] |
Geocoding is the task of converting location mentions in text into structured
data that encodes the geospatial semantics. We propose a new architecture for
geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to
generate a list of candidate entries from the geospatial ontology. Then it
reranks the candidate entries using a transformer-based neural network that
incorporates information from the ontology such as the entry's population. This
generate-and-rerank process is applied twice: first to resolve the less
ambiguous countries, states, and counties, and second to resolve the remaining
location mentions, using the identified countries, states, and counties as
context. Our proposed toponym resolution framework achieves state-of-the-art
performance on multiple datasets. Code and models are available at
\url{https://github.com/clulab/geonorm}.
|
[
"cs.CL"
] | false |
2305.10709
|
2023-05-18T05:01:04Z
|
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in
Natural Language Processing
|
[
"Tingting Wu",
"Xiao Ding",
"Minji Tang",
"Hao Zhang",
"Bing Qin",
"Ting Liu"
] |
Large-scale datasets in the real world inevitably involve label noise. Deep
models can gradually overfit noisy labels and thus degrade model
generalization. To mitigate the effects of label noise, learning with noisy
labels (LNL) methods are designed to achieve better generalization performance.
Due to the lack of suitable datasets, previous studies have frequently employed
synthetic label noise to mimic real-world label noise. However, synthetic noise
is not instance-dependent, making this approximation not always effective in
practice. Recent research has proposed benchmarks for learning with real-world
noisy labels. However, the noise sources within may be single or fuzzy, making
benchmarks different from data with heterogeneous label noises in the real
world. To tackle these issues, we contribute NoisywikiHow, the largest NLP
benchmark built with minimal supervision. Specifically, inspired by human
cognition, we explicitly construct multiple sources of label noise to imitate
human errors throughout the annotation, replicating real-world noise, whose
corruption is affected by both ground-truth labels and instances. Moreover, we
provide a variety of noise levels to support controlled experiments on noisy
data, enabling us to evaluate LNL methods systematically and comprehensively.
After that, we conduct extensive multi-dimensional experiments on a broad range
of LNL methods, obtaining new and intriguing findings.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.10736
|
2023-05-18T06:15:45Z
|
Counterfactual Debiasing for Generating Factually Consistent Text
Summaries
|
[
"Chenhe Dong",
"Yuexiang Xie",
"Yaliang Li",
"Ying Shen"
] |
Despite substantial progress in abstractive text summarization to generate
fluent and informative texts, the factual inconsistency in the generated
summaries remains an important yet challenging problem to be solved. In this
paper, we construct causal graphs for abstractive text summarization and
identify the intrinsic causes of the factual inconsistency, i.e., the language
bias and irrelevancy bias, and further propose a debiasing framework, named
CoFactSum, to alleviate the causal effects of these biases by counterfactual
estimation. Specifically, the proposed CoFactSum provides two counterfactual
estimation strategies, i.e., Explicit Counterfactual Masking with an explicit
dynamic masking strategy, and Implicit Counterfactual Training with an implicit
discriminative cross-attention mechanism. Meanwhile, we design a Debiasing
Degree Adjustment mechanism to dynamically adapt the debiasing degree at each
decoding step. Extensive experiments on two widely-used summarization datasets
demonstrate the effectiveness of CoFactSum in enhancing the factual consistency
of generated summaries compared with several baselines.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.10835
|
2023-05-18T09:24:53Z
|
Ahead-of-Time P-Tuning
|
[
"Daniil Gavrilov",
"Nikita Balagansky"
] |
In this paper, we propose Ahead-of-Time (AoT) P-Tuning, a novel
parameter-efficient fine-tuning method for pre-trained Language Models (LMs)
that adds input-dependent bias before each Transformer layer. We evaluate AoT
P-Tuning on GLUE and SuperGLUE benchmarking datasets using RoBERTa and DeBERTa
models, showing that it outperforms BitFit and is comparable or better than
other baseline methods for efficient fine-tuning. Additionally, we assess the
inference overhead of AoT P-Tuning and demonstrate that it introduces
negligible overhead compared to established baseline methods. Our method
enables multi-task inference with a single backbone LM, making it a practical
solution for real-world applications.
|
[
"cs.LG",
"cs.CL"
] | false |
2305.10920
|
2023-05-18T12:31:45Z
|
Emergent Communication with Attention
|
[
"Ryokan Ri",
"Ryo Ueda",
"Jason Naradowsky"
] |
To develop computational agents that better communicate using their own
emergent language, we endow the agents with an ability to focus their attention
on particular concepts in the environment. Humans often understand an object or
scene as a composite of concepts and those concepts are further mapped onto
words. We implement this intuition as cross-modal attention mechanisms in
Speaker and Listener agents in a referential game and show attention leads to
more compositional and interpretable emergent language. We also demonstrate how
attention aids in understanding the learned communication protocol by
investigating the attention weights associated with each message symbol and the
alignment of attention weights between Speaker and Listener agents. Overall,
our results suggest that attention is a promising mechanism for developing more
human-like emergent language.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.10928
|
2023-05-18T12:40:41Z
|
Multilingual Event Extraction from Historical Newspaper Adverts
|
[
"Nadav Borenstein",
"Natalia da Silva Perez",
"Isabelle Augenstein"
] |
NLP methods can aid historians in analyzing textual materials in greater
volumes than manually feasible. Developing such methods poses substantial
challenges though. First, acquiring large, annotated historical datasets is
difficult, as only domain experts can reliably label them. Second, most
available off-the-shelf NLP models are trained on modern language texts,
rendering them significantly less effective when applied to historical corpora.
This is particularly problematic for less well studied tasks, and for languages
other than English. This paper addresses these challenges while focusing on the
under-explored task of event extraction from a novel domain of historical
texts. We introduce a new multilingual dataset in English, French, and Dutch
composed of newspaper ads from the early modern colonial period reporting on
enslaved people who liberated themselves from enslavement. We find that: 1)
even with scarce annotated data, it is possible to achieve surprisingly good
results by formulating the problem as an extractive QA task and leveraging
existing datasets and models for modern languages; and 2) cross-lingual
low-resource learning for historical languages is highly challenging, and
machine translation of the historical datasets to the considered target
languages is, in practice, often the best-performing solution.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.10991
|
2023-05-18T14:09:52Z
|
Less is More! A slim architecture for optimal language translation
|
[
"Luca Herranz-Celotti",
"Ermal Rrapaj"
] |
The softmax attention mechanism has emerged as a noteworthy development in
the field of Artificial Intelligence research, building on the successes of
Transformer-based architectures. However, their ever increasing sizes
necessitate ever increasing computational memory, that limits their usage. We
propose KgV, a sigmoid gating mechanism that, in conjunction with softmax
attention, significantly boosts performance without increasing architecture
size. To amend the size requirements, we leverage Tensor Chains to identify and
prune the excess parameters. We find that such excess resides primarily within
the embedding layer, and not in the output linear layer. To further improve
embedding and significantly reduce parameters, we introduce H-SoftPOS, a
hierarchical embedding layer which simultaneously enhances performance.
Remarkably, on the WMT14 English-German validation set, our approach yields a
threefold reduction in perplexity, surpassing the current state-of-the-art,
while reducing parameter counts also by a factor of 3. When we further reduce
the number of parameters up to sevenfold, we can still achieve a 21\% decrease
in perplexity with respect to the baseline Transformer. To understand
generalization capabilities, we conduct experiments on the 7 language pairs of
the WMT17 dataset. Our method outperforms existing techniques in terms of test
loss while simultaneously halving the number of parameters. Moreover, we
observe a 70 times reduction in variance with respect to the prior
state-of-the-art. In conclusion, our proposed method yields significant
improvements in performance and much lower memory cost. We call the resulting
architecture Anthe.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.10992
|
2023-05-18T14:11:57Z
|
How does the task complexity of masked pretraining objectives affect
downstream performance?
|
[
"Atsuki Yamaguchi",
"Hiroaki Ozaki",
"Terufumi Morishita",
"Gaku Morio",
"Yasuhiro Sogawa"
] |
Masked language modeling (MLM) is a widely used self-supervised pretraining
objective, where a model needs to predict an original token that is replaced
with a mask given contexts. Although simpler and computationally efficient
pretraining objectives, e.g., predicting the first character of a masked token,
have recently shown comparable results to MLM, no objectives with a masking
scheme actually outperform it in downstream tasks. Motivated by the assumption
that their lack of complexity plays a vital role in the degradation, we
validate whether more complex masked objectives can achieve better results and
investigate how much complexity they should have to perform comparably to MLM.
Our results using GLUE, SQuAD, and Universal Dependencies benchmarks
demonstrate that more complicated objectives tend to show better downstream
results with at least half of the MLM complexity needed to perform comparably
to MLM. Finally, we discuss how we should pretrain a model using a masked
objective from the task complexity perspective.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.11052
|
2023-05-18T15:43:09Z
|
BERM: Training the Balanced and Extractable Representation for Matching
to Improve Generalization Ability of Dense Retrieval
|
[
"Shicheng Xu",
"Liang Pang",
"Huawei Shen",
"Xueqi Cheng"
] |
Dense retrieval has shown promise in the first-stage retrieval process when
trained on in-domain labeled datasets. However, previous studies have found
that dense retrieval is hard to generalize to unseen domains due to its weak
modeling of domain-invariant and interpretable feature (i.e., matching signal
between two texts, which is the essence of information retrieval). In this
paper, we propose a novel method to improve the generalization of dense
retrieval via capturing matching signal called BERM. Fully fine-grained
expression and query-oriented saliency are two properties of the matching
signal. Thus, in BERM, a single passage is segmented into multiple units and
two unit-level requirements are proposed for representation as the constraint
in training to obtain the effective matching signal. One is semantic unit
balance and the other is essential matching unit extractability. Unit-level
view and balanced semantics make representation express the text in a
fine-grained manner. Essential matching unit extractability makes passage
representation sensitive to the given query to extract the pure matching
information from the passage containing complex context. Experiments on BEIR
show that our method can be effectively combined with different dense retrieval
training methods (vanilla, hard negatives mining and knowledge distillation) to
improve its generalization ability without any additional inference overhead
and target domain data.
|
[
"cs.IR",
"cs.CL"
] | false |
2305.11072
|
2023-05-18T15:59:36Z
|
Self-supervised Fine-tuning for Improved Content Representations by
Speaker-invariant Clustering
|
[
"Heng-Jui Chang",
"Alexander H. Liu",
"James Glass"
] |
Self-supervised speech representation models have succeeded in various tasks,
but improving them for content-related problems using unlabeled data is
challenging. We propose speaker-invariant clustering (Spin), a novel
self-supervised learning method that clusters speech representations and
performs swapped prediction between the original and speaker-perturbed
utterances. Spin disentangles speaker information and preserves content
representations with just 45 minutes of fine-tuning on a single GPU. Spin
improves pre-trained networks and outperforms prior methods in speech
recognition and acoustic unit discovery.
|
[
"cs.CL",
"eess.AS"
] | false |
2305.11334
|
2023-05-18T22:47:06Z
|
Writing your own book: A method for going from closed to open book QA to
improve robustness and performance of smaller LLMs
|
[
"Giorgi Kokaia",
"Pratyush Sinha",
"Yutong Jiang",
"Nozha Boujemaa"
] |
We introduce two novel methods, Tree-Search and Self-contextualizing QA,
designed to enhance the performance of large language models (LLMs) in
question-answering tasks. Tree-Search is a sampling technique specifically
created to extract diverse information from an LLM for a given prompt.
Self-contextualizing QA leverages Tree-Search to enable the model to create its
own context using a wide range of information relevant to the prompt, evaluate
it explicitly and return a open book answer to the initial prompt . We
demonstrate that the quality of generated answers improves according to various
metrics, including accuracy, informativeness, coherence, and consistency, as
evaluated by GPT3.5(text-davinci-003). Furthermore, we show that our methods
result in increased robustness and that performance is positively correlated
with tree size, benefiting both answer quality and robustness. Finally, we
discuss other promising applications of Tree-Search, highlighting its potential
to enhance a broad range of tasks beyond question-answering.
\noindent We also discuss several areas for future work, including refining
the Tree-Search and Self-Contextualizing QA methods, improving the coherence of
the generated context, and investigating the impact of bootstrapping on model
robustness
|
[
"cs.CL",
"cs.AI"
] | false |
2305.11349
|
2023-05-18T23:49:31Z
|
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak
Signals
|
[
"Amila Silva",
"Ling Luo",
"Shanika Karunasekera",
"Christopher Leckie"
] |
The emergence of social media as one of the main platforms for people to
access news has enabled the wide dissemination of fake news. This has motivated
numerous studies on automating fake news detection. Although there have been
limited attempts at unsupervised fake news detection, their performance suffers
due to not exploiting the knowledge from various modalities related to news
records and due to the presence of various latent biases in the existing news
datasets. To address these limitations, this work proposes an effective
framework for unsupervised fake news detection, which first embeds the
knowledge available in four modalities in news records and then proposes a
novel noise-robust self-supervised learning technique to identify the veracity
of news records from the multi-modal embeddings. Also, we propose a novel
technique to construct news datasets minimizing the latent biases in existing
news datasets. Following the proposed approach for dataset construction, we
produce a Large-scale Unlabelled News Dataset consisting 419,351 news articles
related to COVID-19, acronymed as LUND-COVID. We trained the proposed
unsupervised framework using LUND-COVID to exploit the potential of large
datasets, and evaluate it using a set of existing labelled datasets. Our
results show that the proposed unsupervised framework largely outperforms
existing unsupervised baselines for different tasks such as multi-modal fake
news detection, fake news early detection and few-shot fake news detection,
while yielding notable improvements for unseen domains during training.
|
[
"cs.LG",
"cs.CL"
] | false |
2305.10647
|
2023-05-18T02:04:38Z
|
BioAug: Conditional Generation based Data Augmentation for Low-Resource
Biomedical NER
|
[
"Sreyan Ghosh",
"Utkarsh Tyagi",
"Sonal Kumar",
"Dinesh Manocha"
] |
Biomedical Named Entity Recognition (BioNER) is the fundamental task of
identifying named entities from biomedical text. However, BioNER suffers from
severe data scarcity and lacks high-quality labeled data due to the highly
specialized and expert knowledge required for annotation. Though data
augmentation has shown to be highly effective for low-resource NER in general,
existing data augmentation techniques fail to produce factual and diverse
augmentations for BioNER. In this paper, we present BioAug, a novel data
augmentation framework for low-resource BioNER. BioAug, built on BART, is
trained to solve a novel text reconstruction task based on selective masking
and knowledge augmentation. Post training, we perform conditional generation
and generate diverse augmentations conditioning BioAug on selectively corrupted
text similar to the training stage. We demonstrate the effectiveness of BioAug
on 5 benchmark BioNER datasets and show that BioAug outperforms all our
baselines by a significant margin (1.5%-21.5% absolute improvement) and is able
to generate augmentations that are both more factual and diverse. Code:
https://github.com/Sreyan88/BioAug.
|
[
"cs.CL",
"cs.AI",
"cs.IR"
] | false |
2305.10649
|
2023-05-18T02:08:33Z
|
ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs
|
[
"Xingchen Song",
"Di Wu",
"Binbin Zhang",
"Zhendong Peng",
"Bo Dang",
"Fuping Pan",
"Zhiyong Wu"
] |
In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding
Prompt-and-Refine strategy (Figure 3), two simple but effective
\textbf{training-free} methods to decrease the Token Display Time (TDT) of
streaming ASR models \textbf{without any accuracy loss}. The core idea of
ZeroPrompt is to append zeroed content to each chunk during inference, which
acts like a prompt to encourage the model to predict future tokens even before
they were spoken. We argue that streaming acoustic encoders naturally have the
modeling ability of Masked Language Models and our experiments demonstrate that
ZeroPrompt is engineering cheap and can be applied to streaming acoustic
encoders on any dataset without any accuracy loss. Specifically, compared with
our baseline models, we achieve 350 $\sim$ 700ms reduction on First Token
Display Time (TDT-F) and 100 $\sim$ 400ms reduction on Last Token Display Time
(TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and
Librispeech datasets.
|
[
"cs.SD",
"cs.CL",
"eess.AS",
"I.2.7"
] | false |
2305.10679
|
2023-05-18T03:32:54Z
|
Think Outside the Code: Brainstorming Boosts Large Language Models in
Code Generation
|
[
"Xin-Ye Li",
"Jiang-Tian Xue",
"Zheng Xie",
"Ming Li"
] |
Code generation aims to automatically generate source code from high-level
task specifications, which can significantly increase productivity of software
engineering. Recently, approaches based on large language models (LLMs) have
shown remarkable code generation abilities on simple tasks. However, generate
code for more complex tasks, such as competition-level problems, remains
challenging. In this paper, we introduce Brainstorm framework for code
generation. It leverages a brainstorming step that generates and selects
diverse thoughts on the problem to facilitate algorithmic reasoning, where the
thoughts are possible blueprint of solving the problem. We demonstrate that
Brainstorm significantly enhances the ability of LLMs to solve
competition-level programming problems, resulting in a more than 50% increase
in the pass@$k$ metrics for ChatGPT on the CodeContests benchmark, achieving
state-of-the-art performance. Furthermore, our experiments conducted on
LeetCode contests show that our framework boosts the ability of ChatGPT to a
level comparable to that of human programmers.
|
[
"cs.AI",
"cs.CL",
"cs.SE"
] | false |
2305.10686
|
2023-05-18T03:57:51Z
|
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis
|
[
"Jinzheng He",
"Jinglin Liu",
"Zhenhui Ye",
"Rongjie Huang",
"Chenye Cui",
"Huadai Liu",
"Zhou Zhao"
] |
We are interested in a challenging task, Realistic-Music-Score based Singing
Voice Synthesis (RMS-SVS). RMS-SVS aims to generate high-quality singing voices
given realistic music scores with different note types (grace, slur, rest,
etc.). Though significant progress has been achieved, recent singing voice
synthesis (SVS) methods are limited to fine-grained music scores, which require
a complicated data collection pipeline with time-consuming manual annotation to
align music notes with phonemes. Furthermore, these manual annotation destroys
the regularity of note durations in music scores, making fine-grained music
scores inconvenient for composing. To tackle these challenges, we propose
RMSSinger, the first RMS-SVS method, which takes realistic music scores as
input, eliminating most of the tedious manual annotation and avoiding the
aforementioned inconvenience. Note that music scores are based on words rather
than phonemes, in RMSSinger, we introduce word-level modeling to avoid the
time-consuming phoneme duration annotation and the complicated phoneme-level
mel-note alignment. Furthermore, we propose the first diffusion-based pitch
modeling method, which ameliorates the naturalness of existing pitch-modeling
methods. To achieve these, we collect a new dataset containing realistic music
scores and singing voices according to these realistic music scores from
professional singers. Extensive experiments on the dataset demonstrate the
effectiveness of our methods. Audio samples are available at
https://rmssinger.github.io/.
|
[
"cs.SD",
"cs.CL",
"eess.AS"
] | false |
2305.10703
|
2023-05-18T04:30:09Z
|
ReGen: Zero-Shot Text Classification via Training Data Generation with
Progressive Dense Retrieval
|
[
"Yue Yu",
"Yuchen Zhuang",
"Rongzhi Zhang",
"Yu Meng",
"Jiaming Shen",
"Chao Zhang"
] |
With the development of large language models (LLMs), zero-shot learning has
attracted much attention for various NLP tasks. Different from prior works that
generate training data with billion-scale natural language generation (NLG)
models, we propose a retrieval-enhanced framework to create training data from
a general-domain unlabeled corpus. To realize this, we first conduct
contrastive pretraining to learn an unsupervised dense retriever for extracting
the most relevant documents using class-descriptive verbalizers. We then
further propose two simple strategies, namely Verbalizer Augmentation with
Demonstrations and Self-consistency Guided Filtering to improve the topic
coverage of the dataset while removing noisy examples. Experiments on nine
datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines
and saves around 70% of the time compared to baselines using large NLG models.
Besides, REGEN can be naturally integrated with recently proposed large
language models to boost performance.
|
[
"cs.CL",
"cs.IR",
"cs.LG"
] | false |
2305.10788
|
2023-05-18T08:00:09Z
|
Whisper-KDQ: A Lightweight Whisper via Guided Knowledge Distillation and
Quantization for Efficient ASR
|
[
"Hang Shao",
"Wei Wang",
"Bei Liu",
"Xun Gong",
"Haoyu Wang",
"Yanmin Qian"
] |
Due to the rapid development of computing hardware resources and the dramatic
growth of data, pre-trained models in speech recognition, such as Whisper, have
significantly improved the performance of speech recognition tasks. However,
these models usually have a high computational overhead, making it difficult to
execute effectively on resource-constrained devices. To speed up inference and
reduce model size while maintaining performance, we propose a novel guided
knowledge distillation and quantization for large pre-trained model Whisper.
The student model selects distillation and quantization layers based on
quantization loss and distillation loss, respectively. We compressed
$\text{Whisper}_\text{small}$ to $\text{Whisper}_\text{base}$ and
$\text{Whisper}_\text{tiny}$ levels, making $\text{Whisper}_\text{small}$
5.18x/10.48x smaller, respectively. Moreover, compared to the original
$\text{Whisper}_\text{base}$ and $\text{Whisper}_\text{tiny}$, there is also a
relative character error rate~(CER) reduction of 11.3% and 14.0% for the new
compressed model respectively.
|
[
"cs.SD",
"cs.CL",
"eess.AS"
] | false |
2305.10834
|
2023-05-18T09:23:05Z
|
AIwriting: Relations Between Image Generation and Digital Writing
|
[
"Scott Rettberg",
"Talan Memmott",
"Jill Walker Rettberg",
"Jason Nelson",
"Patrick Lichty"
] |
During 2022, both transformer-based AI text generation sys-tems such as GPT-3
and AI text-to-image generation systems such as DALL-E 2 and Stable Diffusion
made exponential leaps forward and are unquestionably altering the fields of
digital art and electronic literature. In this panel a group of electronic
literature authors and theorists consider new oppor-tunities for human
creativity presented by these systems and present new works have produced
during the past year that specifically address these systems as environments
for literary expressions that are translated through iterative interlocutive
processes into visual representations. The premise that binds these
presentations is that these systems and the works gener-ated must be considered
from a literary perspective, as they originate in human writing. In works
ranging from a visual memoir of the personal experience of a health crisis, to
interac-tive web comics, to architectures based on abstract poetic language, to
political satire, four artists explore the capabili-ties of these writing
environments for new genres of literary artist practice, while a digital
culture theorist considers the origins and effects of the particular training
datasets of human language and images on which these new hybrid forms are
based.
|
[
"cs.AI",
"cs.CL",
"cs.HC",
"cs.MM",
"J.5"
] | false |
2305.10839
|
2023-05-18T09:50:47Z
|
A Lexical-aware Non-autoregressive Transformer-based ASR Model
|
[
"Chong-En Lin",
"Kuan-Yu Chen"
] |
Non-autoregressive automatic speech recognition (ASR) has become a mainstream
of ASR modeling because of its fast decoding speed and satisfactory result. To
further boost the performance, relaxing the conditional independence assumption
and cascading large-scaled pre-trained models are two active research
directions. In addition to these strategies, we propose a lexical-aware
non-autoregressive Transformer-based (LA-NAT) ASR framework, which consists of
an acoustic encoder, a speech-text shared encoder, and a speech-text shared
decoder. The acoustic encoder is used to process the input speech features as
usual, and the speech-text shared encoder and decoder are designed to train
speech and text data simultaneously. By doing so, LA-NAT aims to make the ASR
model aware of lexical information, so the resulting model is expected to
achieve better results by leveraging the learned linguistic knowledge. A series
of experiments are conducted on the AISHELL-1, CSJ, and TEDLIUM 2 datasets.
According to the experiments, the proposed LA-NAT can provide superior results
than other recently proposed non-autoregressive ASR models. In addition, LA-NAT
is a relatively compact model than most non-autoregressive ASR models, and it
is about 58 times faster than the classic autoregressive model.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.10923
|
2023-05-18T12:37:01Z
|
Query Performance Prediction: From Ad-hoc to Conversational Search
|
[
"Chuan Meng",
"Negar Arabzadeh",
"Mohammad Aliannejadi",
"Maarten de Rijke"
] |
Query performance prediction (QPP) is a core task in information retrieval.
The QPP task is to predict the retrieval quality of a search system for a query
without relevance judgments. Research has shown the effectiveness and
usefulness of QPP for ad-hoc search. Recent years have witnessed considerable
progress in conversational search (CS). Effective QPP could help a CS system to
decide an appropriate action to be taken at the next turn. Despite its
potential, QPP for CS has been little studied. We address this research gap by
reproducing and studying the effectiveness of existing QPP methods in the
context of CS. While the task of passage retrieval remains the same in the two
settings, a user query in CS depends on the conversational history, introducing
novel QPP challenges. In particular, we seek to explore to what extent findings
from QPP methods for ad-hoc search generalize to three CS settings: (i)
estimating the retrieval quality of different query rewriting-based retrieval
methods, (ii) estimating the retrieval quality of a conversational dense
retrieval method, and (iii) estimating the retrieval quality for top ranks vs.
deeper-ranked lists. Our findings can be summarized as follows: (i) supervised
QPP methods distinctly outperform unsupervised counterparts only when a
large-scale training set is available; (ii) point-wise supervised QPP methods
outperform their list-wise counterparts in most cases; and (iii) retrieval
score-based unsupervised QPP methods show high effectiveness in assessing the
conversational dense retrieval method, ConvDR.
|
[
"cs.IR",
"cs.CL",
"cs.LG",
"H.3.3"
] | false |
2305.11013
|
2023-05-18T14:45:09Z
|
FunASR: A Fundamental End-to-End Speech Recognition Toolkit
|
[
"Zhifu Gao",
"Zerui Li",
"Jiaming Wang",
"Haoneng Luo",
"Xian Shi",
"Mengzhe Chen",
"Yabin Li",
"Lingyun Zuo",
"Zhihao Du",
"Zhangyu Xiao",
"Shiliang Zhang"
] |
This paper introduces FunASR, an open-source speech recognition toolkit
designed to bridge the gap between academic research and industrial
applications. FunASR offers models trained on large-scale industrial corpora
and the ability to deploy them in applications. The toolkit's flagship model,
Paraformer, is a non-autoregressive end-to-end speech recognition model that
has been trained on a manually annotated Mandarin speech recognition dataset
that contains 60,000 hours of speech. To improve the performance of Paraformer,
we have added timestamp prediction and hotword customization capabilities to
the standard Paraformer backbone. In addition, to facilitate model deployment,
we have open-sourced a voice activity detection model based on the Feedforward
Sequential Memory Network (FSMN-VAD) and a text post-processing punctuation
model based on the controllable time-delay Transformer (CT-Transformer), both
of which were trained on industrial corpora. These functional modules provide a
solid foundation for building high-precision long audio speech recognition
services. Compared to other models trained on open datasets, Paraformer
demonstrates superior performance.
|
[
"cs.SD",
"cs.CL",
"eess.AS"
] | false |
2305.11073
|
2023-05-18T16:00:48Z
|
A Comparative Study on E-Branchformer vs Conformer in Speech
Recognition, Translation, and Understanding Tasks
|
[
"Yifan Peng",
"Kwangyoun Kim",
"Felix Wu",
"Brian Yan",
"Siddhant Arora",
"William Chen",
"Jiyang Tang",
"Suwon Shon",
"Prashant Sridhar",
"Shinji Watanabe"
] |
Conformer, a convolution-augmented Transformer variant, has become the de
facto encoder architecture for speech processing due to its superior
performance in various tasks, including automatic speech recognition (ASR),
speech translation (ST) and spoken language understanding (SLU). Recently, a
new encoder called E-Branchformer has outperformed Conformer in the LibriSpeech
ASR benchmark, making it promising for more general speech applications. This
work compares E-Branchformer and Conformer through extensive experiments using
different types of end-to-end sequence-to-sequence models. Results demonstrate
that E-Branchformer achieves comparable or better performance than Conformer in
almost all evaluation sets across 15 ASR, 2 ST, and 3 SLU benchmarks, while
being more stable during training. We will release our training configurations
and pre-trained models for reproducibility, which can benefit the speech
community.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.11170
|
2023-05-18T17:58:31Z
|
Efficient Prompting via Dynamic In-Context Learning
|
[
"Wangchunshu Zhou",
"Yuchen Eleanor Jiang",
"Ryan Cotterell",
"Mrinmaya Sachan"
] |
The primary way of building AI applications is shifting from training
specialist models to prompting generalist models. A common practice for
prompting generalist models, often referred to as in-context learning, is to
append a few examples (demonstrations) to the prompt to help the model better
understand the task. While effective, in-context learning can be inefficient
because it makes the input prompt much longer, consuming valuable space in the
context window and leading to larger computational costs. In this paper, we
propose DynaICL, a recipe for efficient prompting with black-box generalist
models that dynamically allocate in-context examples according to the input
complexity and the computational budget. To achieve this, we train a meta
controller that predicts the number of in-context examples suitable for the
generalist model to make a good prediction based on the performance-efficiency
trade-off for a specific input. We then dynamically allocate the number of
demonstrations for an input according to predictions from the meta controller
and the given computation budget. Experimental results show that dynamic
example allocation helps achieve a better performance-efficiency trade-off in
two practical settings where computational resources or the required
performance is constrained. Specifically, DynaICL saves up to 46% token budget
compared to the common practice that allocates the same number of in-context
examples to each input. We also find that a meta controller trained on a
certain backbone model and tasks can successfully generalize to unseen models
and tasks.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.11206
|
2023-05-18T17:45:22Z
|
LIMA: Less Is More for Alignment
|
[
"Chunting Zhou",
"Pengfei Liu",
"Puxin Xu",
"Srini Iyer",
"Jiao Sun",
"Yuning Mao",
"Xuezhe Ma",
"Avia Efrat",
"Ping Yu",
"Lili Yu",
"Susan Zhang",
"Gargi Ghosh",
"Mike Lewis",
"Luke Zettlemoyer",
"Omer Levy"
] |
Large language models are trained in two stages: (1) unsupervised pretraining
from raw text, to learn general-purpose representations, and (2) large scale
instruction tuning and reinforcement learning, to better align to end tasks and
user preferences. We measure the relative importance of these two stages by
training LIMA, a 65B parameter LLaMa language model fine-tuned with the
standard supervised loss on only 1,000 carefully curated prompts and responses,
without any reinforcement learning or human preference modeling. LIMA
demonstrates remarkably strong performance, learning to follow specific
response formats from only a handful of examples in the training data,
including complex queries that range from planning trip itineraries to
speculating about alternate history. Moreover, the model tends to generalize
well to unseen tasks that did not appear in the training data. In a controlled
human study, responses from LIMA are either equivalent or strictly preferred to
GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard
and 65% versus DaVinci003, which was trained with human feedback. Taken
together, these results strongly suggest that almost all knowledge in large
language models is learned during pretraining, and only limited instruction
tuning data is necessary to teach models to produce high quality output.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | true |
2305.10690
|
2023-05-18T04:01:40Z
|
Sampling, Diffusions, and Stochastic Localization
|
[
"Andrea Montanari"
] |
Diffusions are a successful technique to sample from high-dimensional
distributions can be either explicitly given or learnt from a collection of
samples. They implement a diffusion process whose endpoint is a sample from the
target distribution and whose drift is typically represented as a neural
network. Stochastic localization is a successful technique to prove mixing of
Markov Chains and other functional inequalities in high dimension. An
algorithmic version of stochastic localization was introduced in [EAMS2022], to
obtain an algorithm that samples from certain statistical mechanics models.
This notes have three objectives: (i) Generalize the construction [EAMS2022]
to other stochastic localization processes; (ii) Clarify the connection between
diffusions and stochastic localization. In particular we show that standard
denoising diffusions are stochastic localizations but other examples that are
naturally suggested by the proposed viewpoint; (iii) Describe some insights
that follow from this viewpoint.
|
[
"cs.LG"
] | false |
2305.10696
|
2023-05-18T04:17:46Z
|
Unbiased Gradient Boosting Decision Tree with Unbiased Feature
Importance
|
[
"Zheyu Zhang",
"Tianping Zhang",
"Jian Li"
] |
Gradient Boosting Decision Tree (GBDT) has achieved remarkable success in a
wide variety of applications. The split finding algorithm, which determines the
tree construction process, is one of the most crucial components of GBDT.
However, the split finding algorithm has long been criticized for its bias
towards features with a large number of potential splits. This bias introduces
severe interpretability and overfitting issues in GBDT. To this end, we provide
a fine-grained analysis of bias in GBDT and demonstrate that the bias
originates from 1) the systematic bias in the gain estimation of each split and
2) the bias in the split finding algorithm resulting from the use of the same
data to evaluate the split improvement and determine the best split. Based on
the analysis, we propose unbiased gain, a new unbiased measurement of gain
importance using out-of-bag samples. Moreover, we incorporate the unbiased
property into the split finding algorithm and develop UnbiasedGBM to solve the
overfitting issue of GBDT. We assess the performance of UnbiasedGBM and
unbiased gain in a large-scale empirical study comprising 60 datasets and show
that: 1) UnbiasedGBM exhibits better performance than popular GBDT
implementations such as LightGBM, XGBoost, and Catboost on average on the 60
datasets and 2) unbiased gain achieves better average performance in feature
selection than popular feature importance methods. The codes are available at
https://github.com/ZheyuAqaZhang/UnbiasedGBM.
|
[
"cs.LG"
] | false |
2305.10730
|
2023-05-18T05:58:24Z
|
FedMR: Federated Learning via Model Recombination
|
[
"Ming Hu",
"Zhihao Yue",
"Zhiwei Ling",
"Yihao Huang",
"Cheng Chen",
"Xian Wei",
"Yang Liu",
"Mingsong Chen"
] |
Although Federated Learning (FL) enables global model training across clients
without compromising their raw data, existing Federated Averaging
(FedAvg)-based methods suffer from the problem of low inference performance,
especially for unevenly distributed data among clients. This is mainly because
i) FedAvg initializes client models with the same global models, which makes
the local training hard to escape from the local search for optimal solutions;
and ii) by averaging model parameters in a coarse manner, FedAvg eclipses the
individual characteristics of local models. To address such issues that
strongly limit the inference capability of FL, we propose a novel and effective
FL paradigm named FedMR (Federated Model Recombination). Unlike conventional
FedAvg-based methods, the cloud server of FedMR shuffles each layer of
collected local models and recombines them to achieve new models for local
training on clients. Due to the diversified initialization models for clients
coupled with fine-grained model recombination, FedMR can converge to a
well-generalized global model for all the clients, leading to a superior
inference performance. Experimental results show that, compared with
state-of-the-art FL methods, FedMR can significantly improve inference accuracy
in a quicker manner without exposing client privacy.
|
[
"cs.LG"
] | false |
2305.11017
|
2023-05-18T14:50:00Z
|
Deep Metric Tensor Regularized Policy Gradient
|
[
"Gang Chen",
"Victoria Huang"
] |
Policy gradient algorithms are an important family of deep reinforcement
learning techniques. Many past research endeavors focused on using the
first-order policy gradient information to train policy networks. Different
from these works, we conduct research in this paper driven by the believe that
properly utilizing and controlling Hessian information associated with the
policy gradient can noticeably improve the performance of policy gradient
algorithms. One key Hessian information that attracted our attention is the
Hessian trace, which gives the divergence of the policy gradient vector field
in the Euclidean policy parametric space. We set the goal to generalize this
Euclidean policy parametric space into a general Riemmanian manifold by
introducing a metric tensor field $g_ab$ in the parametric space. This is
achieved through newly developed mathematical tools, deep learning algorithms,
and metric tensor deep neural networks (DNNs). Armed with these technical
developments, we propose a new policy gradient algorithm that learns to
minimize the absolute divergence in the Riemannian manifold as an important
regularization mechanism, allowing the Riemannian manifold to smoothen its
policy gradient vector field. The newly developed algorithm is experimentally
studied on several benchmark reinforcement learning problems. Our experiments
clearly show that the new metric tensor regularized algorithm can significantly
outperform its counterpart that does not use our regularization technique.
Additional experimental analysis further suggests that the trained metric
tensor DNN and the corresponding metric tensor $g_{ab}$ can effectively reduce
the absolute divergence towards zero in the Riemannian manifold.
|
[
"cs.LG"
] | false |
2305.11213
|
2023-05-18T18:00:00Z
|
Information-Ordered Bottlenecks for Adaptive Semantic Compression
|
[
"Matthew Ho",
"Xiaosheng Zhao",
"Benjamin Wandelt"
] |
We present the information-ordered bottleneck (IOB), a neural layer designed
to adaptively compress data into latent variables ordered by likelihood
maximization. Without retraining, IOB nodes can be truncated at any bottleneck
width, capturing the most crucial information in the first latent variables.
Unifying several previous approaches, we show that IOBs achieve near-optimal
compression for a given encoding architecture and can assign ordering to latent
signals in a manner that is semantically meaningful. IOBs demonstrate a
remarkable ability to compress embeddings of image and text data, leveraging
the performance of SOTA architectures such as CNNs, transformers, and diffusion
models. Moreover, we introduce a novel theory for estimating global intrinsic
dimensionality with IOBs and show that they recover SOTA dimensionality
estimates for complex synthetic data. Furthermore, we showcase the utility of
these models for exploratory analysis through applications on heterogeneous
datasets, enabling computer-aided discovery of dataset complexity.
|
[
"cs.LG"
] | false |
2305.11300
|
2023-05-18T20:48:50Z
|
Bayesian Risk-Averse Q-Learning with Streaming Observations
|
[
"Yuhao Wang",
"Enlu Zhou"
] |
We consider a robust reinforcement learning problem, where a learning agent
learns from a simulated training environment. To account for the model
mis-specification between this training environment and the real environment
due to lack of data, we adopt a formulation of Bayesian risk MDP (BRMDP) with
infinite horizon, which uses Bayesian posterior to estimate the transition
model and impose a risk functional to account for the model uncertainty.
Observations from the real environment that is out of the agent's control
arrive periodically and are utilized by the agent to update the Bayesian
posterior to reduce model uncertainty. We theoretically demonstrate that BRMDP
balances the trade-off between robustness and conservativeness, and we further
develop a multi-stage Bayesian risk-averse Q-learning algorithm to solve BRMDP
with streaming observations from real environment. The proposed algorithm
learns a risk-averse yet optimal policy that depends on the availability of
real-world observations. We provide a theoretical guarantee of strong
convergence for the proposed algorithm.
|
[
"cs.LG"
] | false |
2305.10634
|
2023-05-18T01:10:42Z
|
Modified Gauss-Newton Algorithms under Noise
|
[
"Krishna Pillutla",
"Vincent Roulet",
"Sham Kakade",
"Zaid Harchaoui"
] |
Gauss-Newton methods and their stochastic version have been widely used in
machine learning and signal processing. Their nonsmooth counterparts, modified
Gauss-Newton or prox-linear algorithms, can lead to contrasting outcomes when
compared to gradient descent in large-scale statistical settings. We explore
the contrasting performance of these two classes of algorithms in theory on a
stylized statistical example, and experimentally on learning problems including
structured prediction. In theory, we delineate the regime where the quadratic
convergence of the modified Gauss-Newton method is active under statistical
noise. In the experiments, we underline the versatility of stochastic
(sub)-gradient descent to minimize nonsmooth composite objectives.
|
[
"math.OC",
"cs.LG"
] | false |
2305.10636
|
2023-05-18T01:13:04Z
|
Augmented Message Passing Stein Variational Gradient Descent
|
[
"Jiankui Zhou",
"Yue Qiu"
] |
Stein Variational Gradient Descent (SVGD) is a popular particle-based method
for Bayesian inference. However, its convergence suffers from the variance
collapse, which reduces the accuracy and diversity of the estimation. In this
paper, we study the isotropy property of finite particles during the
convergence process and show that SVGD of finite particles cannot spread across
the entire sample space. Instead, all particles tend to cluster around the
particle center within a certain range and we provide an analytical bound for
this cluster. To further improve the effectiveness of SVGD for high-dimensional
problems, we propose the Augmented Message Passing SVGD (AUMP-SVGD) method,
which is a two-stage optimization procedure that does not require sparsity of
the target distribution, unlike the MP-SVGD method. Our algorithm achieves
satisfactory accuracy and overcomes the variance collapse problem in various
benchmark problems.
|
[
"cs.LG",
"stat.ML",
"62-08, 62G09",
"G.3; I.2"
] | false |
2305.10673
|
2023-05-18T03:23:53Z
|
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic
Graphs
|
[
"Jintang Li",
"Sheng Tian",
"Ruofan Wu",
"Liang Zhu",
"Welong Zhao",
"Changhua Meng",
"Liang Chen",
"Zibin Zheng",
"Hongzhi Yin"
] |
The prevalence of large-scale graphs poses great challenges in time and
storage for training and deploying graph neural networks (GNNs). Several recent
works have explored solutions for pruning the large original graph into a small
and highly-informative one, such that training and inference on the pruned and
large graphs have comparable performance. Although empirically effective,
current researches focus on static or non-temporal graphs, which are not
directly applicable to dynamic scenarios. In addition, they require labels as
ground truth to learn the informative structure, limiting their applicability
to new problem domains where labels are hard to obtain. To solve the dilemma,
we propose and study the problem of unsupervised graph pruning on dynamic
graphs. We approach the problem by our proposed STEP, a self-supervised
temporal pruning framework that learns to remove potentially redundant edges
from input dynamic graphs. From a technical and industrial viewpoint, our
method overcomes the trade-offs between the performance and the time & memory
overheads. Our results on three real-world datasets demonstrate the advantages
on improving the efficacy, robustness, and efficiency of GNNs on dynamic node
classification tasks. Most notably, STEP is able to prune more than 50% of
edges on a million-scale industrial graph Alipay (7M nodes, 21M edges) while
approximating up to 98% of the original performance. Code is available at
https://github.com/EdisonLeeeee/STEP.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.10681
|
2023-05-18T03:37:29Z
|
Black-Box Targeted Reward Poisoning Attack Against Online Deep
Reinforcement Learning
|
[
"Yinglun Xu",
"Gagandeep Singh"
] |
We propose the first black-box targeted attack against online deep
reinforcement learning through reward poisoning during training time. Our
attack is applicable to general environments with unknown dynamics learned by
unknown algorithms and requires limited attack budgets and computational
resources. We leverage a general framework and find conditions to ensure
efficient attack under a general assumption of the learning algorithms. We show
that our attack is optimal in our framework under the conditions. We
experimentally verify that with limited budgets, our attack efficiently leads
the learning agent to various target policies under a diverse set of popular
DRL environments and state-of-the-art learners.
|
[
"cs.LG",
"cs.CR"
] | false |
2305.10693
|
2023-05-18T04:07:47Z
|
Gated Deeper Models are Effective Factor Learners
|
[
"Jingjing Guo"
] |
Precisely forecasting the excess returns of an asset (e.g., Tesla stock) is
beneficial to all investors. However, the unpredictability of market dynamics,
influenced by human behaviors, makes this a challenging task. In prior
research, researcher have manually crafted among of factors as signals to guide
their investing process. In contrast, this paper view this problem in a
different perspective that we align deep learning model to combine those human
designed factors to predict the trend of excess returns. To this end, we
present a 5-layer deep neural network that generates more meaningful factors in
a 2048-dimensional space. Modern network design techniques are utilized to
enhance robustness training and reduce overfitting. Additionally, we propose a
gated network that dynamically filters out noise-learned features, resulting in
improved performance. We evaluate our model over 2,000 stocks from the China
market with their recent three years records. The experimental results show
that the proposed gated activation layer and the deep neural network could
effectively overcome the problem. Specifically, the proposed gated activation
layer and deep neural network contribute to the superior performance of our
model. In summary, the proposed model exhibits promising results and could
potentially benefit investors seeking to optimize their investment strategies.
|
[
"q-fin.PR",
"cs.LG"
] | false |
2305.10706
|
2023-05-18T04:46:49Z
|
A Framework Based on Symbolic Regression Coupled with eXtended
Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion
from Data
|
[
"Elham Kiyani",
"Khemraj Shukla",
"George Em Karniadakis",
"Mikko Karttunen"
] |
We propose a framework and an algorithm to uncover the unknown parts of
nonlinear equations directly from data. The framework is based on eXtended
Physics-Informed Neural Networks (X-PINNs), domain decomposition in space-time,
but we augment the original X-PINN method by imposing flux continuity across
the domain interfaces. The well-known Allen-Cahn equation is used to
demonstrate the approach. The Frobenius matrix norm is used to evaluate the
accuracy of the X-PINN predictions and the results show excellent performance.
In addition, symbolic regression is employed to determine the closed form of
the unknown part of the equation from the data, and the results confirm the
accuracy of the X-PINNs based approach. To test the framework in a situation
resembling real-world data, random noise is added to the datasets to mimic
scenarios such as the presence of thermal noise or instrument errors. The
results show that the framework is stable against significant amount of noise.
As the final part, we determine the minimal amount of data required for
training the neural network. The framework is able to predict the correct form
and coefficients of the underlying dynamical equation when at least 50\% data
is used for training.
|
[
"cond-mat.dis-nn",
"cs.LG"
] | false |
2305.10716
|
2023-05-18T05:27:46Z
|
A Survey on Time-Series Pre-Trained Models
|
[
"Qianli Ma",
"Zhen Liu",
"Zhenjing Zheng",
"Ziyang Huang",
"Siying Zhu",
"Zhongzhong Yu",
"James T. Kwok"
] |
Time-Series Mining (TSM) is an important research area since it shows great
potential in practical applications. Deep learning models that rely on massive
labeled data have been utilized for TSM successfully. However, constructing a
large-scale well-labeled dataset is difficult due to data annotation costs.
Recently, Pre-Trained Models have gradually attracted attention in the time
series domain due to their remarkable performance in computer vision and
natural language processing. In this survey, we provide a comprehensive review
of Time-Series Pre-Trained Models (TS-PTMs), aiming to guide the understanding,
applying, and studying TS-PTMs. Specifically, we first briefly introduce the
typical deep learning models employed in TSM. Then, we give an overview of
TS-PTMs according to the pre-training techniques. The main categories we
explore include supervised, unsupervised, and self-supervised TS-PTMs. Further,
extensive experiments are conducted to analyze the advantages and disadvantages
of transfer learning strategies, Transformer-based models, and representative
TS-PTMs. Finally, we point out some potential directions of TS-PTMs for future
work.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.10721
|
2023-05-18T05:39:46Z
|
Revisiting Long-term Time Series Forecasting: An Investigation on Linear
Mapping
|
[
"Zhe Li",
"Shiyi Qi",
"Yiduo Li",
"Zenglin Xu"
] |
Long-term time series forecasting has gained significant attention in recent
years. While there are various specialized designs for capturing temporal
dependency, previous studies have demonstrated that a single linear layer can
achieve competitive forecasting performance compared to other complex
architectures. In this paper, we thoroughly investigate the intrinsic
effectiveness of recent approaches and make three key observations: 1) linear
mapping is critical to prior long-term time series forecasting efforts; 2)
RevIN (reversible normalization) and CI (Channel Independent) play a vital role
in improving overall forecasting performance; and 3) linear mapping can
effectively capture periodic features in time series and has robustness for
different periods across channels when increasing the input horizon. We provide
theoretical and experimental explanations to support our findings and also
discuss the limitations and future works. Our framework's code is available at
\url{https://github.com/plumprc/RTSF}.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.10760
|
2023-05-18T07:01:58Z
|
Automatic Design Method of Building Pipeline Layout Based on Deep
Reinforcement Learning
|
[
"Chen Yang",
"Zhe Zheng",
"Jia-Rui Lin"
] |
The layout design of pipelines is a critical task in the construction
industry. Currently, pipeline layout is designed manually by engineers, which
is time-consuming and laborious. Automating and streamlining this process can
reduce the burden on engineers and save time. In this paper, we propose a
method for generating three-dimensional layout of pipelines based on deep
reinforcement learning (DRL). Firstly, we abstract the geometric features of
space to establish a training environment and define reward functions based on
three constraints: pipeline length, elbow, and installation distance. Next, we
collect data through interactions between the agent and the environment and
train the DRL model. Finally, we use the well-trained DRL model to
automatically design a single pipeline. Our results demonstrate that DRL models
can complete the pipeline layout task in space in a much shorter time than
traditional algorithms while ensuring high-quality layout outcomes.
|
[
"cs.LG",
"cs.NE"
] | false |
2305.10840
|
2023-05-18T09:52:06Z
|
Uncertainty Quantification in Deep Neural Networks through Statistical
Inference on Latent Space
|
[
"Luigi Sbailò",
"Luca M. Ghiringhelli"
] |
Uncertainty-quantification methods are applied to estimate the confidence of
deep-neural-networks classifiers over their predictions. However, most widely
used methods are known to be overconfident. We address this problem by
developing an algorithm that exploits the latent-space representation of data
points fed into the network, to assess the accuracy of their prediction. Using
the latent-space representation generated by the fraction of training set that
the network classifies correctly, we build a statistical model that is able to
capture the likelihood of a given prediction. We show on a synthetic dataset
that commonly used methods are mostly overconfident. Overconfidence occurs also
for predictions made on data points that are outside the distribution that
generated the training data. In contrast, our method can detect such
out-of-distribution data points as inaccurately predicted, thus aiding in the
automatic detection of outliers.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.10987
|
2023-05-18T14:06:37Z
|
SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking
Neural Networks
|
[
"Henrique Branquinho",
"Nuno Lourenço",
"Ernesto Costa"
] |
Spiking Neural Networks (SNNs) have attracted recent interest due to their
energy efficiency and biological plausibility. However, the performance of SNNs
still lags behind traditional Artificial Neural Networks (ANNs), as there is no
consensus on the best learning algorithm for SNNs. Best-performing SNNs are
based on ANN to SNN conversion or learning with spike-based backpropagation
through surrogate gradients. The focus of recent research has been on
developing and testing different learning strategies, with hand-tailored
architectures and parameter tuning. Neuroevolution (NE), has proven successful
as a way to automatically design ANNs and tune parameters, but its applications
to SNNs are still at an early stage. DENSER is a NE framework for the automatic
design and parametrization of ANNs, based on the principles of Genetic
Algorithms (GA) and Structured Grammatical Evolution (SGE). In this paper, we
propose SPENSER, a NE framework for SNN generation based on DENSER, for image
classification on the MNIST and Fashion-MNIST datasets. SPENSER generates
competitive performing networks with a test accuracy of 99.42% and 91.65%
respectively.
|
[
"cs.NE",
"cs.LG"
] | false |
2305.10994
|
2023-05-18T14:14:42Z
|
Understanding how Differentially Private Generative Models Spend their
Privacy Budget
|
[
"Georgi Ganev",
"Kai Xu",
"Emiliano De Cristofaro"
] |
Generative models trained with Differential Privacy (DP) are increasingly
used to produce synthetic data while reducing privacy risks. Navigating their
specific privacy-utility tradeoffs makes it challenging to determine which
models would work best for specific settings/tasks. In this paper, we fill this
gap in the context of tabular data by analyzing how DP generative models
distribute privacy budgets across rows and columns, arguably the main source of
utility degradation. We examine the main factors contributing to how privacy
budgets are spent, including underlying modeling techniques, DP mechanisms, and
data dimensionality.
Our extensive evaluation of both graphical and deep generative models sheds
light on the distinctive features that render them suitable for different
settings and tasks. We show that graphical models distribute the privacy budget
horizontally and thus cannot handle relatively wide datasets while the
performance on the task they were optimized for monotonically increases with
more data. Deep generative models spend their budget per iteration, so their
behavior is less predictable with varying dataset dimensions but could perform
better if trained on more features. Also, low levels of privacy
($\epsilon\geq100$) could help some models generalize, achieving better results
than without applying DP.
|
[
"cs.LG",
"cs.CR"
] | false |
2305.11039
|
2023-05-18T15:32:32Z
|
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial
Network Packet Generation
|
[
"Soumyadeep Hore",
"Jalal Ghadermazi",
"Diwas Paudel",
"Ankit Shah",
"Tapas K. Das",
"Nathaniel D. Bastian"
] |
Recent advancements in artificial intelligence (AI) and machine learning (ML)
algorithms, coupled with the availability of faster computing infrastructure,
have enhanced the security posture of cybersecurity operations centers
(defenders) through the development of ML-aided network intrusion detection
systems (NIDS). Concurrently, the abilities of adversaries to evade security
have also increased with the support of AI/ML models. Therefore, defenders need
to proactively prepare for evasion attacks that exploit the detection
mechanisms of NIDS. Recent studies have found that the perturbation of
flow-based and packet-based features can deceive ML models, but these
approaches have limitations. Perturbations made to the flow-based features are
difficult to reverse-engineer, while samples generated with perturbations to
the packet-based features are not playable.
Our methodological framework, Deep PackGen, employs deep reinforcement
learning to generate adversarial packets and aims to overcome the limitations
of approaches in the literature. By taking raw malicious network packets as
inputs and systematically making perturbations on them, Deep PackGen
camouflages them as benign packets while still maintaining their functionality.
In our experiments, using publicly available data, Deep PackGen achieved an
average adversarial success rate of 66.4\% against various ML models and across
different attack types. Our investigation also revealed that more than 45\% of
the successful adversarial samples were out-of-distribution packets that evaded
the decision boundaries of the classifiers. The knowledge gained from our study
on the adversary's ability to make specific evasive perturbations to different
types of malicious packets can help defenders enhance the robustness of their
NIDS against evolving adversarial attacks.
|
[
"cs.CR",
"cs.LG"
] | false |
2305.11055
|
2023-05-18T15:50:33Z
|
Small noise analysis for Tikhonov and RKHS regularizations
|
[
"Quanjun Lang",
"Fei Lu"
] |
Regularization plays a pivotal role in ill-posed machine learning and inverse
problems. However, the fundamental comparative analysis of various
regularization norms remains open. We establish a small noise analysis
framework to assess the effects of norms in Tikhonov and RKHS regularizations,
in the context of ill-posed linear inverse problems with Gaussian noise. This
framework studies the convergence rates of regularized estimators in the small
noise limit and reveals the potential instability of the conventional
L2-regularizer. We solve such instability by proposing an innovative class of
adaptive fractional RKHS regularizers, which covers the L2 Tikhonov and RKHS
regularizations by adjusting the fractional smoothness parameter. A surprising
insight is that over-smoothing via these fractional RKHSs consistently yields
optimal convergence rates, but the optimal hyper-parameter may decay too fast
to be selected in practice.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.11056
|
2023-05-18T15:50:54Z
|
PETAL: Physics Emulation Through Averaged Linearizations for Solving
Inverse Problems
|
[
"Jihui Jin",
"Etienne Ollivier",
"Richard Touret",
"Matthew McKinley",
"Karim G. Sabra",
"Justin K. Romberg"
] |
Inverse problems describe the task of recovering an underlying signal of
interest given observables. Typically, the observables are related via some
non-linear forward model applied to the underlying unknown signal. Inverting
the non-linear forward model can be computationally expensive, as it often
involves computing and inverting a linearization at a series of estimates.
Rather than inverting the physics-based model, we instead train a surrogate
forward model (emulator) and leverage modern auto-grad libraries to solve for
the input within a classical optimization framework. Current methods to train
emulators are done in a black box supervised machine learning fashion and fail
to take advantage of any existing knowledge of the forward model. In this
article, we propose a simple learned weighted average model that embeds
linearizations of the forward model around various reference points into the
model itself, explicitly incorporating known physics. Grounding the learned
model with physics based linearizations improves the forward modeling accuracy
and provides richer physics based gradient information during the inversion
process leading to more accurate signal recovery. We demonstrate the efficacy
on an ocean acoustic tomography (OAT) example that aims to recover ocean sound
speed profile (SSP) variations from acoustic observations (e.g. eigenray
arrival times) within simulation of ocean dynamics in the Gulf of Mexico.
|
[
"eess.SP",
"cs.LG"
] | false |
2305.11084
|
2023-05-18T16:13:41Z
|
Preference or Intent? Double Disentangled Collaborative Filtering
|
[
"Chao Wang",
"Hengshu Zhu",
"Dazhong Shen",
"Wei wu",
"Hui Xiong"
] |
People usually have different intents for choosing items, while their
preferences under the same intent may also different. In traditional
collaborative filtering approaches, both intent and preference factors are
usually entangled in the modeling process, which significantly limits the
robustness and interpretability of recommendation performances. For example,
the low-rating items are always treated as negative feedback while they
actually could provide positive information about user intent. To this end, in
this paper, we propose a two-fold representation learning approach, namely
Double Disentangled Collaborative Filtering (DDCF), for personalized
recommendations. The first-level disentanglement is for separating the
influence factors of intent and preference, while the second-level
disentanglement is performed to build independent sparse preference
representations under individual intent with limited computational complexity.
Specifically, we employ two variational autoencoder networks, intent
recognition network and preference decomposition network, to learn the intent
and preference factors, respectively. In this way, the low-rating items will be
treated as positive samples for modeling intents while the negative samples for
modeling preferences. Finally, extensive experiments on three real-world
datasets and four evaluation metrics clearly validate the effectiveness and the
interpretability of DDCF.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.11097
|
2023-05-18T16:34:21Z
|
Statistical Foundations of Prior-Data Fitted Networks
|
[
"Thomas Nagler"
] |
Prior-data fitted networks (PFNs) were recently proposed as a new paradigm
for machine learning. Instead of training the network to an observed training
set, a fixed model is pre-trained offline on small, simulated training sets
from a variety of tasks. The pre-trained model is then used to infer class
probabilities in-context on fresh training sets with arbitrary size and
distribution. Empirically, PFNs achieve state-of-the-art performance on tasks
with similar size to the ones used in pre-training. Surprisingly, their
accuracy further improves when passed larger data sets during inference. This
article establishes a theoretical foundation for PFNs and illuminates the
statistical mechanisms governing their behavior. While PFNs are motivated by
Bayesian ideas, a purely frequentistic interpretation of PFNs as pre-tuned, but
untrained predictors explains their behavior. A predictor's variance vanishes
if its sensitivity to individual training samples does and the bias vanishes
only if it is appropriately localized around the test feature. The transformer
architecture used in current PFN implementations ensures only the former. These
findings shall prove useful for designing architectures with favorable
empirical behavior.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.11108
|
2023-05-18T16:52:43Z
|
MiraBest: A Dataset of Morphologically Classified Radio Galaxies for
Machine Learning
|
[
"Fiona A. M. Porter",
"Anna M. M. Scaife"
] |
The volume of data from current and future observatories has motivated the
increased development and application of automated machine learning
methodologies for astronomy. However, less attention has been given to the
production of standardised datasets for assessing the performance of different
machine learning algorithms within astronomy and astrophysics. Here we describe
in detail the MiraBest dataset, a publicly available batched dataset of 1256
radio-loud AGN from NVSS and FIRST, filtered to $0.03 < z < 0.1$, manually
labelled by Miraghaei and Best (2017) according to the Fanaroff-Riley
morphological classification, created for machine learning applications and
compatible for use with standard deep learning libraries. We outline the
principles underlying the construction of the dataset, the sample selection and
pre-processing methodology, dataset structure and composition, as well as a
comparison of MiraBest to other datasets used in the literature. Existing
applications that utilise the MiraBest dataset are reviewed, and an extended
dataset of 2100 sources is created by cross-matching MiraBest with other
catalogues of radio-loud AGN that have been used more widely in the literature
for machine learning applications.
|
[
"astro-ph.IM",
"cs.LG"
] | false |
2305.11197
|
2023-05-18T14:06:06Z
|
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
|
[
"Yichen Zhu",
"Jian Yuan",
"Bo Jiang",
"Tao Lin",
"Haiming Jin",
"Xinbing Wang",
"Chenghu Zhou"
] |
Data with missing values is ubiquitous in many applications. Recent years
have witnessed increasing attention on prediction with only incomplete data
consisting of observed features and a mask that indicates the missing pattern.
Existing methods assume that the training and testing distributions are the
same, which may be violated in real-world scenarios. In this paper, we consider
prediction with incomplete data in the presence of distribution shift. We focus
on the case where the underlying joint distribution of complete features and
label is invariant, but the missing pattern, i.e., mask distribution may shift
agnostically between training and testing. To achieve generalization, we
leverage the observation that for each mask, there is an invariant optimal
predictor. To avoid the exponential explosion when learning them separately, we
approximate the optimal predictors jointly using a double parameterization
technique. This has the undesirable side effect of allowing the learned
predictors to rely on the intra-mask correlation and that between features and
mask. We perform decorrelation to minimize this effect. Combining the
techniques above, we propose a novel prediction method called StableMiss.
Extensive experiments on both synthetic and real-world datasets show that
StableMiss is robust and outperforms state-of-the-art methods under agnostic
mask distribution shift.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.11199
|
2023-05-18T14:55:27Z
|
At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19
Patients Using Statistical and Machine Learning Methods: An International
Cohort Study
|
[
"Munib Mesinovic",
"Xin Ci Wong",
"Giri Shan Rajahram",
"Barbara Wanjiru Citarella",
"Kalaiarasu M. Peariasamy",
"Frank van Someren Greve",
"Piero Olliaro",
"Laura Merson",
"Lei Clifton",
"Christiana Kartsonaki",
"ISARIC Characterisation Group"
] |
By September, 2022, more than 600 million cases of SARS-CoV-2 infection have
been reported globally, resulting in over 6.5 million deaths. COVID-19
mortality risk estimators are often, however, developed with small
unrepresentative samples and with methodological limitations. It is highly
important to develop predictive tools for pulmonary embolism (PE) in COVID-19
patients as one of the most severe preventable complications of COVID-19. Using
a dataset of more than 800,000 COVID-19 patients from an international cohort,
we propose a cost-sensitive gradient-boosted machine learning model that
predicts occurrence of PE and death at admission. Logistic regression, Cox
proportional hazards models, and Shapley values were used to identify key
predictors for PE and death. Our prediction model had a test AUROC of 75.9% and
74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality
respectively on a highly diverse and held-out test set. The PE prediction model
was also evaluated on patients in UK and Spain separately with test results of
74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex,
region of admission, comorbidities (chronic cardiac and pulmonary disease,
dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any,
confusion, chest pain, fatigue, headache, fever, muscle or joint pain,
shortness of breath) were the most important clinical predictors at admission.
Our machine learning model developed from an international cohort can serve to
better regulate hospital risk prioritisation of at-risk patients.
|
[
"q-bio.QM",
"cs.LG"
] | false |
2305.11311
|
2023-05-18T21:22:23Z
|
BELLA: Black box model Explanations by Local Linear Approximations
|
[
"Nedeljko Radulovic",
"Albert Bifet",
"Fabian Suchanek"
] |
In recent years, understanding the decision-making process of black-box
models has become not only a legal requirement but also an additional way to
assess their performance. However, the state of the art post-hoc interpretation
approaches rely on synthetic data generation. This introduces uncertainty and
can hurt the reliability of the interpretations. Furthermore, they tend to
produce explanations that apply to only very few data points. This makes the
explanations brittle and limited in scope. Finally, they provide scores that
have no direct verifiable meaning. In this paper, we present BELLA, a
deterministic model-agnostic post-hoc approach for explaining the individual
predictions of regression black-box models. BELLA provides explanations in the
form of a linear model trained in the feature space. Thus, its coefficients can
be used directly to compute the predicted value from the feature values.
Furthermore, BELLA maximizes the size of the neighborhood to which the linear
model applies, so that the explanations are accurate, simple, general, and
robust. BELLA can produce both factual and counterfactual explanations. Our
user study confirms the importance of the desiderata we optimize, and our
experiments show that BELLA outperforms the state-of-the-art approaches on
these desiderata.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.11340
|
2023-05-18T23:23:08Z
|
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning
with Energy-based Models
|
[
"Wenhao Ding",
"Tong Che",
"Ding Zhao",
"Marco Pavone"
] |
Recently, reward-conditioned reinforcement learning (RCRL) has gained
popularity due to its simplicity, flexibility, and off-policy nature. However,
we will show that current RCRL approaches are fundamentally limited and fail to
address two critical challenges of RCRL -- improving generalization on high
reward-to-go (RTG) inputs, and avoiding out-of-distribution (OOD) RTG queries
during testing time. To address these challenges when training vanilla RCRL
architectures, we propose Bayesian Reparameterized RCRL (BR-RCRL), a novel set
of inductive biases for RCRL inspired by Bayes' theorem. BR-RCRL removes a core
obstacle preventing vanilla RCRL from generalizing on high RTG inputs -- a
tendency that the model treats different RTG inputs as independent values,
which we term ``RTG Independence". BR-RCRL also allows us to design an
accompanying adaptive inference method, which maximizes total returns while
avoiding OOD queries that yield unpredictable behaviors in vanilla RCRL
methods. We show that BR-RCRL achieves state-of-the-art performance on the
Gym-Mujoco and Atari offline RL benchmarks, improving upon vanilla RCRL by up
to 11%.
|
[
"cs.LG",
"cs.RO"
] | false |
2305.13329
|
2023-05-18T03:17:24Z
|
Classification of Orbits in Poincaré Maps using Machine Learning
|
[
"Chandrika Kamath"
] |
Poincar\'e plots, also called Poincar\'e maps, are used by plasma physicists
to understand the behavior of magnetically confined plasma in numerical
simulations of a tokamak. These plots are created by the intersection of field
lines with a two-dimensional poloidal plane that is perpendicular to the axis
of the torus representing the tokamak. A plot is composed of multiple orbits,
each created by a different field line as it goes around the torus. Each orbit
can have one of four distinct shapes, or classes, that indicate changes in the
topology of the magnetic fields confining the plasma. Given the (x,y)
coordinates of the points that form an orbit, the analysis task is to assign a
class to the orbit, a task that appears ideally suited for a machine learning
approach. In this paper, we describe how we overcame two major challenges in
solving this problem - creating a high-quality training set, with few
mislabeled orbits, and converting the coordinates of the points into features
that are discriminating, despite the variation within the orbits of a class and
the apparent similarities between orbits of different classes. Our automated
approach is not only more objective and accurate than visual classification,
but is also less tedious, making it easier for plasma physicists to analyze the
topology of magnetic fields from numerical simulations of the tokamak.
|
[
"physics.plasm-ph",
"cs.LG"
] | false |
2305.14368
|
2023-05-18T03:26:39Z
|
Support for Stock Trend Prediction Using Transformers and Sentiment
Analysis
|
[
"Harsimrat Kaeley",
"Ye Qiao",
"Nader Bagherzadeh"
] |
Stock trend analysis has been an influential time-series prediction topic due
to its lucrative and inherently chaotic nature. Many models looking to
accurately predict the trend of stocks have been based on Recurrent Neural
Networks (RNNs). However, due to the limitations of RNNs, such as gradient
vanish and long-term dependencies being lost as sequence length increases, in
this paper we develop a Transformer based model that uses technical stock data
and sentiment analysis to conduct accurate stock trend prediction over long
time windows. This paper also introduces a novel dataset containing daily
technical stock data and top news headline data spanning almost three years.
Stock prediction based solely on technical data can suffer from lag caused by
the inability of stock indicators to effectively factor in breaking market
news. The use of sentiment analysis on top headlines can help account for
unforeseen shifts in market conditions caused by news coverage. We measure the
performance of our model against RNNs over sequence lengths spanning 5 business
days to 30 business days to mimic different length trading strategies. This
reveals an improvement in directional accuracy over RNNs as sequence length is
increased, with the largest improvement being close to 18.63% at 30 business
days.
|
[
"q-fin.ST",
"cs.LG"
] | false |
2306.00720
|
2023-05-18T17:48:46Z
|
Neural Bee Colony Optimization: A Case Study in Public Transit Network
Design
|
[
"Andrew Holliday",
"Gregory Dudek"
] |
In this work we explore the combination of metaheuristics and learned neural
network solvers for combinatorial optimization. We do this in the context of
the transit network design problem, a uniquely challenging combinatorial
optimization problem with real-world importance. We train a neural network
policy to perform single-shot planning of individual transit routes, and then
incorporate it as one of several sub-heuristics in a modified Bee Colony
Optimization (BCO) metaheuristic algorithm. Our experimental results
demonstrate that this hybrid algorithm outperforms the learned policy alone by
up to 20% and the original BCO algorithm by up to 53% on realistic problem
instances. We perform a set of ablations to study the impact of each component
of the modified algorithm.
|
[
"cs.NE",
"cs.LG"
] | false |
2306.03741
|
2023-05-18T03:08:18Z
|
Pre-training Tensor-Train Networks Facilitates Machine Learning with
Variational Quantum Circuits
|
[
"Jun Qi",
"Chao-Han Huck Yang",
"Pin-Yu Chen",
"Min-Hsiu Hsieh"
] |
Variational quantum circuit (VQC) is a promising approach for implementing
quantum neural networks on noisy intermediate-scale quantum (NISQ) devices.
Recent studies have shown that a tensor-train network (TTN) for VQC, namely
TTN-VQC, can improve the representation and generalization powers of VQC.
However, the Barren Plateau problem leads to the gradients of the cost function
vanishing exponentially small as the number of qubits increases, making it
difficult to find the optimal parameters for the VQC. To address this issue, we
put forth a new learning approach called Pre+TTN-VQC that builds upon the
TTN-VQC architecture by incorporating a pre-trained TTN to alleviate the Barren
Plateau problem. The pre-trained TTN allows for efficient fine-tuning of target
data, which reduces the depth of the VQC required to achieve good empirical
performance and potentially alleviates the training obstacles posed by the
Barren Plateau landscape. Furthermore, we highlight the advantages of
Pre+TTN-VQC in terms of representation and generalization powers by exploiting
the error performance analysis. Moreover, we characterize the optimization
performance of Pre+TTN-VQC without the need for the Polyak-Lojasiewicz
condition, thereby enhancing the practicality of implementing quantum neural
networks on NISQ devices. We conduct experiments on a handwritten digit
classification dataset to corroborate our proposed methods and theorems.
|
[
"quant-ph",
"cs.LG"
] | false |
2306.07974
|
2023-05-18T21:16:59Z
|
Chainlet Orbits: Topological Address Embedding for the Bitcoin
Blockchain
|
[
"Poupak Azad",
"Baris Coskunuzer",
"Murat Kantarcioglu",
"Cuneyt Gurcan Akcora"
] |
The rise of cryptocurrencies like Bitcoin, which enable transactions with a
degree of pseudonymity, has led to a surge in various illicit activities,
including ransomware payments and transactions on darknet markets. These
illegal activities often utilize Bitcoin as the preferred payment method.
However, current tools for detecting illicit behavior either rely on a few
heuristics and laborious data collection processes or employ computationally
inefficient graph neural network (GNN) models that are challenging to
interpret.
To overcome the computational and interpretability limitations of existing
techniques, we introduce an effective solution called Chainlet Orbits. This
approach embeds Bitcoin addresses by leveraging their topological
characteristics in transactions. By employing our innovative address embedding,
we investigate e-crime in Bitcoin networks by focusing on distinctive
substructures that arise from illicit behavior.
The results of our node classification experiments demonstrate superior
performance compared to state-of-the-art methods, including both topological
and GNN-based approaches. Moreover, our approach enables the use of
interpretable and explainable machine learning models in as little as 15
minutes for most days on the Bitcoin transaction network.
|
[
"cs.CR",
"cs.LG"
] | false |
2309.03907
|
2023-05-18T16:22:33Z
|
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule
Graphs
|
[
"Youwei Liang",
"Ruiyi Zhang",
"Li Zhang",
"Pengtao Xie"
] |
A ChatGPT-like system for drug compounds could be a game-changer in
pharmaceutical research, accelerating drug discovery, enhancing our
understanding of structure-activity relationships, guiding lead optimization,
aiding drug repurposing, reducing the failure rate, and streamlining clinical
trials. In this work, we make an initial attempt towards enabling ChatGPT-like
capabilities on drug molecule graphs, by developing a prototype system
DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
molecule graph and ask various questions about this compound. DrugChat will
answer these questions in a multi-turn, interactive manner. The DrugChat system
consists of a graph neural network (GNN), a large language model (LLM), and an
adaptor. The GNN takes a compound molecule graph as input and learns a
representation for this graph. The adaptor transforms the graph representation
produced by the GNN into another representation that is acceptable to the LLM.
The LLM takes the compound representation transformed by the adaptor and users'
questions about this compound as inputs and generates answers. All these
components are trained end-to-end. To train DrugChat, we collected instruction
tuning datasets which contain 10,834 drug compounds and 143,517 question-answer
pairs. The code and data is available at
\url{https://github.com/UCSD-AI4H/drugchat}
|
[
"q-bio.BM",
"cs.LG"
] | true |
2305.10633
|
2023-05-18T01:10:11Z
|
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample
Complexity for Learning Single Index Models
|
[
"Alex Damian",
"Eshaan Nichani",
"Rong Ge",
"Jason D. Lee"
] |
We focus on the task of learning a single index model $\sigma(w^\star \cdot
x)$ with respect to the isotropic Gaussian distribution in $d$ dimensions.
Prior work has shown that the sample complexity of learning $w^\star$ is
governed by the information exponent $k^\star$ of the link function $\sigma$,
which is defined as the index of the first nonzero Hermite coefficient of
$\sigma$. Ben Arous et al. (2021) showed that $n \gtrsim d^{k^\star-1}$ samples
suffice for learning $w^\star$ and that this is tight for online SGD. However,
the CSQ lower bound for gradient based methods only shows that $n \gtrsim
d^{k^\star/2}$ samples are necessary. In this work, we close the gap between
the upper and lower bounds by showing that online SGD on a smoothed loss learns
$w^\star$ with $n \gtrsim d^{k^\star/2}$ samples. We also draw connections to
statistical analyses of tensor PCA and to the implicit regularization effects
of minibatch SGD on empirical losses.
|
[
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] | false |
2305.10659
|
2023-05-18T02:42:59Z
|
Use of Speech Impairment Severity for Dysarthric Speech Recognition
|
[
"Mengzhe Geng",
"Zengrui Jin",
"Tianzi Wang",
"Shujie Hu",
"Jiajun Deng",
"Mingyu Cui",
"Guinan Li",
"Jianwei Yu",
"Xurong Xie",
"Xunying Liu"
] |
A key challenge in dysarthric speech recognition is the speaker-level
diversity attributed to both speaker-identity associated factors such as
gender, and speech impairment severity. Most prior researches on addressing
this issue focused on using speaker-identity only. To this end, this paper
proposes a novel set of techniques to use both severity and speaker-identity in
dysarthric speech recognition: a) multitask training incorporating severity
prediction error; b) speaker-severity aware auxiliary feature adaptation; and
c) structured LHUC transforms separately conditioned on speaker-identity and
severity. Experiments conducted on UASpeech suggest incorporating additional
speech impairment severity into state-of-the-art hybrid DNN, E2E Conformer and
pre-trained Wav2vec 2.0 ASR systems produced statistically significant WER
reductions up to 4.78% (14.03% relative). Using the best system the lowest
published WER of 17.82% (51.25% on very low intelligibility) was obtained on
UASpeech.
|
[
"eess.AS",
"cs.AI",
"cs.LG",
"cs.SD"
] | false |
2305.10668
|
2023-05-18T03:04:51Z
|
MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection
|
[
"Xiongxiao Xu",
"Kaize Ding",
"Canyu Chen",
"Kai Shu"
] |
Graph anomaly detection has long been an important problem in various domains
pertaining to information security such as financial fraud, social spam,
network intrusion, etc. The majority of existing methods are performed in an
unsupervised manner, as labeled anomalies in a large scale are often too
expensive to acquire. However, the identified anomalies may turn out to be data
noises or uninteresting data instances due to the lack of prior knowledge on
the anomalies. In realistic scenarios, it is often feasible to obtain limited
labeled anomalies, which have great potential to advance graph anomaly
detection. However, the work exploring limited labeled anomalies and a large
amount of unlabeled nodes in graphs to detect anomalies is rather limited.
Therefore, in this paper, we study a novel problem of few-shot graph anomaly
detection. We propose a new framework MetaGAD to learn to meta-transfer the
knowledge between unlabeled and labeled nodes for graph anomaly detection.
Experimental results on six real-world datasets with synthetic anomalies and
"organic" anomalies (available in the dataset) demonstrate the effectiveness of
the proposed approach in detecting anomalies with limited labeled anomalies.
|
[
"cs.LG",
"cs.AI",
"cs.CR",
"cs.SI"
] | false |
2305.10698
|
2023-05-18T04:19:26Z
|
Ranking the locations and predicting future crime occurrence by
retrieving news from different Bangla online newspapers
|
[
"Jumman Hossain",
"Rajib Chandra Das",
"Md. Ruhul Amin",
"Md. Saiful Islam"
] |
There have thousands of crimes are happening daily all around. But people
keep statistics only few of them, therefore crime rates are increasing day by
day. The reason behind can be less concern or less statistics of previous
crimes. It is much more important to observe the previous crime statistics for
general people to make their outing decision and police for catching the
criminals are taking steps to restrain the crimes and tourists to make their
travelling decision. National institute of justice releases crime survey data
for the country, but does not offer crime statistics up to Union or Thana
level. Considering all of these cases we have come up with an approach which
can give an approximation to people about the safety of a specific location
with crime ranking of different areas locating the crimes on a map including a
future crime occurrence prediction mechanism. Our approach relies on different
online Bangla newspapers for crawling the crime data, stemming and keyword
extraction, location finding algorithm, cosine similarity, naive Bayes
classifier, and a custom crime prediction model
|
[
"cs.IR",
"cs.CY",
"cs.LG"
] | false |
2305.10823
|
2023-05-18T09:05:17Z
|
FastFit: Towards Real-Time Iterative Neural Vocoder by Replacing U-Net
Encoder With Multiple STFTs
|
[
"Won Jang",
"Dan Lim",
"Heayoung Park"
] |
This paper presents FastFit, a novel neural vocoder architecture that
replaces the U-Net encoder with multiple short-time Fourier transforms (STFTs)
to achieve faster generation rates without sacrificing sample quality. We
replaced each encoder block with an STFT, with parameters equal to the temporal
resolution of each decoder block, leading to the skip connection. FastFit
reduces the number of parameters and the generation time of the model by almost
half while maintaining high fidelity. Through objective and subjective
evaluations, we demonstrated that the proposed model achieves nearly twice the
generation speed of baseline iteration-based vocoders while maintaining high
sound quality. We further showed that FastFit produces sound qualities similar
to those of other baselines in text-to-speech evaluation scenarios, including
multi-speaker and zero-shot text-to-speech.
|
[
"eess.AS",
"cs.LG",
"cs.SD"
] | false |
2305.10863
|
2023-05-18T10:34:23Z
|
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving
with Workload Awareness
|
[
"Zeyuan Tan",
"Xiulong Yuan",
"Congjie He",
"Man-Kit Sit",
"Guo Li",
"Xiaoze Liu",
"Baole Ai",
"Kai Zeng",
"Peter Pietzuch",
"Luo Mai"
] |
Systems for serving inference requests on graph neural networks (GNN) must
combine low latency with high throughout, but they face irregular computation
due to skew in the number of sampled graph nodes and aggregated GNN features.
This makes it challenging to exploit GPUs effectively: using GPUs to sample
only a few graph nodes yields lower performance than CPU-based sampling; and
aggregating many features exhibits high data movement costs between GPUs and
CPUs. Therefore, current GNN serving systems use CPUs for graph sampling and
feature aggregation, limiting throughput.
We describe Quiver, a distributed GPU-based GNN serving system with
low-latency and high-throughput. Quiver's key idea is to exploit workload
metrics for predicting the irregular computation of GNN requests, and governing
the use of GPUs for graph sampling and feature aggregation: (1) for graph
sampling, Quiver calculates the probabilistic sampled graph size, a metric that
predicts the degree of parallelism in graph sampling. Quiver uses this metric
to assign sampling tasks to GPUs only when the performance gains surpass
CPU-based sampling; and (2) for feature aggregation, Quiver relies on the
feature access probability to decide which features to partition and replicate
across a distributed GPU NUMA topology. We show that Quiver achieves up to 35
times lower latency with an 8 times higher throughput compared to
state-of-the-art GNN approaches (DGL and PyG).
|
[
"cs.DC",
"cs.AI",
"cs.LG",
"cs.OS"
] | false |
2305.10886
|
2023-05-18T11:27:02Z
|
Minimum-Risk Recalibration of Classifiers
|
[
"Zeyu Sun",
"Dogyoon Song",
"Alfred Hero"
] |
Recalibrating probabilistic classifiers is vital for enhancing the
reliability and accuracy of predictive models. Despite the development of
numerous recalibration algorithms, there is still a lack of a comprehensive
theory that integrates calibration and sharpness (which is essential for
maintaining predictive power). In this paper, we introduce the concept of
minimum-risk recalibration within the framework of mean-squared-error (MSE)
decomposition, offering a principled approach for evaluating and recalibrating
probabilistic classifiers. Using this framework, we analyze the uniform-mass
binning (UMB) recalibration method and establish a finite-sample risk upper
bound of order $\tilde{O}(B/n + 1/B^2)$ where $B$ is the number of bins and $n$
is the sample size. By balancing calibration and sharpness, we further
determine that the optimal number of bins for UMB scales with $n^{1/3}$,
resulting in a risk bound of approximately $O(n^{-2/3})$. Additionally, we
tackle the challenge of label shift by proposing a two-stage approach that
adjusts the recalibration function using limited labeled data from the target
domain. Our results show that transferring a calibrated classifier requires
significantly fewer target samples compared to recalibrating from scratch. We
validate our theoretical findings through numerical simulations, which confirm
the tightness of the proposed bounds, the optimal number of bins, and the
effectiveness of label shift adaptation.
|
[
"cs.LG",
"stat.ME",
"stat.ML"
] | false |
2305.10931
|
2023-05-18T12:46:42Z
|
Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered
by Reconfigurable Intelligent Surfaces
|
[
"Kyriakos Stylianopoulos",
"Mattia Merluzzi",
"Paolo Di Lorenzo",
"George C. Alexandropoulos"
] |
In this paper, we propose a novel algorithm for energy-efficient,
low-latency, accurate inference at the wireless edge, in the context of 6G
networks endowed with reconfigurable intelligent surfaces (RISs). We consider a
scenario where new data are continuously generated/collected by a set of
devices and are handled through a dynamic queueing system. Building on the
marriage between Lyapunov stochastic optimization and deep reinforcement
learning (DRL), we devise a dynamic learning algorithm that jointly optimizes
the data compression scheme, the allocation of radio resources (i.e., power,
transmission precoding), the computation resources (i.e., CPU cycles), and the
RIS reflectivity parameters (i.e., phase shifts), with the aim of performing
energy-efficient edge classification with end-to-end (E2E) delay and inference
accuracy constraints. The proposed strategy enables dynamic control of the
system and of the wireless propagation environment, performing a low-complexity
optimization on a per-slot basis while dealing with time-varying radio channels
and task arrivals, whose statistics are unknown. Numerical results assess the
performance of the proposed RIS-empowered edge inference strategy in terms of
trade-off between energy, delay, and accuracy of a classification task.
|
[
"cs.IT",
"cs.ET",
"cs.LG",
"math.IT"
] | false |
2305.10952
|
2023-05-18T13:21:38Z
|
Actor-Critic Methods using Physics-Informed Neural Networks: Control of
a 1D PDE Model for Fluid-Cooled Battery Packs
|
[
"Amartya Mukherjee",
"Jun Liu"
] |
This paper proposes an actor-critic algorithm for controlling the temperature
of a battery pack using a cooling fluid. This is modeled by a coupled 1D
partial differential equation (PDE) with a controlled advection term that
determines the speed of the cooling fluid. The Hamilton-Jacobi-Bellman (HJB)
equation is a PDE that evaluates the optimality of the value function and
determines an optimal controller. We propose an algorithm that treats the value
network as a Physics-Informed Neural Network (PINN) to solve for the
continuous-time HJB equation rather than a discrete-time Bellman optimality
equation, and we derive an optimal controller for the environment that we
exploit to achieve optimal control. Our experiments show that a hybrid-policy
method that updates the value network using the HJB equation and updates the
policy network identically to PPO achieves the best results in the control of
this PDE system.
|
[
"cs.LG",
"math.AP",
"math.OC"
] | false |
2305.10997
|
2023-05-18T14:19:19Z
|
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
|
[
"Saptarshi Nath",
"Christos Peridis",
"Eseoghene Ben-Iwhiwhu",
"Xinran Liu",
"Shirin Dora",
"Cong Liu",
"Soheil Kolouri",
"Andrea Soltoggio"
] |
Lifelong learning agents aim to learn multiple tasks sequentially over a
lifetime. This involves the ability to exploit previous knowledge when learning
new tasks and to avoid forgetting. Modulating masks, a specific type of
parameter isolation approach, have recently shown promise in both supervised
and reinforcement learning. While lifelong learning algorithms have been
investigated mainly within a single-agent approach, a question remains on how
multiple agents can share lifelong learning knowledge with each other. We show
that the parameter isolation mechanism used by modulating masks is particularly
suitable for exchanging knowledge among agents in a distributed and
decentralized system of lifelong learners. The key idea is that the isolation
of specific task knowledge to specific masks allows agents to transfer only
specific knowledge on-demand, resulting in robust and effective distributed
lifelong learning. We assume fully distributed and asynchronous scenarios with
dynamic agent numbers and connectivity. An on-demand communication protocol
ensures agents query their peers for specific masks to be transferred and
integrated into their policies when facing each task. Experiments indicate that
on-demand mask communication is an effective way to implement distributed
lifelong reinforcement learning and provides a lifelong learning benefit with
respect to distributed RL baselines such as DD-PPO, IMPALA, and PPO+EWC. The
system is particularly robust to connection drops and demonstrates rapid
learning due to knowledge exchange.
|
[
"cs.LG",
"cs.AI",
"cs.DC",
"cs.MA"
] | false |
2305.11005
|
2023-05-18T14:36:07Z
|
Mode Connectivity in Auction Design
|
[
"Christoph Hertrich",
"Yixin Tao",
"László A. Végh"
] |
Optimal auction design is a fundamental problem in algorithmic game theory.
This problem is notoriously difficult already in very simple settings. Recent
work in differentiable economics showed that neural networks can efficiently
learn known optimal auction mechanisms and discover interesting new ones. In an
attempt to theoretically justify their empirical success, we focus on one of
the first such networks, RochetNet, and a generalized version for affine
maximizer auctions. We prove that they satisfy mode connectivity, i.e., locally
optimal solutions are connected by a simple, piecewise linear path such that
every solution on the path is almost as good as one of the two local optima.
Mode connectivity has been recently investigated as an intriguing empirical and
theoretically justifiable property of neural networks used for prediction
problems. Our results give the first such analysis in the context of
differentiable economics, where neural networks are used directly for solving
non-convex optimization problems.
|
[
"cs.GT",
"cs.LG",
"cs.NE",
"stat.ML"
] | false |
2305.11022
|
2023-05-18T15:03:56Z
|
Massively Parallel Reweighted Wake-Sleep
|
[
"Thomas Heap",
"Gavin Leech",
"Laurence Aitchison"
] |
Reweighted wake-sleep (RWS) is a machine learning method for performing
Bayesian inference in a very general class of models. RWS draws $K$ samples
from an underlying approximate posterior, then uses importance weighting to
provide a better estimate of the true posterior. RWS then updates its
approximate posterior towards the importance-weighted estimate of the true
posterior. However, recent work [Chattergee and Diaconis, 2018] indicates that
the number of samples required for effective importance weighting is
exponential in the number of latent variables. Attaining such a large number of
importance samples is intractable in all but the smallest models. Here, we
develop massively parallel RWS, which circumvents this issue by drawing $K$
samples of all $n$ latent variables, and individually reasoning about all $K^n$
possible combinations of samples. While reasoning about $K^n$ combinations
might seem intractable, the required computations can be performed in
polynomial time by exploiting conditional independencies in the generative
model. We show considerable improvements over standard "global" RWS, which
draws $K$ samples from the full joint.
|
[
"cs.LG",
"cs.NE",
"stat.ML"
] | false |
2305.11040
|
2023-05-18T15:34:14Z
|
Simulation of a Variational Quantum Perceptron using Grover's Algorithm
|
[
"Nouhaila Innan",
"Mohamed Bennai"
] |
The quantum perceptron, the variational circuit, and the Grover algorithm
have been proposed as promising components for quantum machine learning. This
paper presents a new quantum perceptron that combines the quantum variational
circuit and the Grover algorithm. However, this does not guarantee that this
quantum variational perceptron with Grover's algorithm (QVPG) will have any
advantage over its quantum variational (QVP) and classical counterparts. Here,
we examine the performance of QVP and QVP-G by computing their loss function
and analyzing their accuracy on the classification task, then comparing these
two quantum models to the classical perceptron (CP). The results show that our
two quantum models are more efficient than CP, and our novel suggested model
QVP-G outperforms the QVP, demonstrating that the Grover can be applied to the
classification task and even makes the model more accurate, besides the
unstructured search problems.
|
[
"quant-ph",
"cs.AI",
"cs.LG"
] | false |
2305.11041
|
2023-05-18T15:35:11Z
|
High-dimensional Asymptotics of Denoising Autoencoders
|
[
"Hugo Cui",
"Lenka Zdeborová"
] |
We address the problem of denoising data from a Gaussian mixture using a
two-layer non-linear autoencoder with tied weights and a skip connection. We
consider the high-dimensional limit where the number of training samples and
the input dimension jointly tend to infinity while the number of hidden units
remains bounded. We provide closed-form expressions for the denoising
mean-squared test error. Building on this result, we quantitatively
characterize the advantage of the considered architecture over the autoencoder
without the skip connection that relates closely to principal component
analysis. We further show that our results accurately capture the learning
curves on a range of real data sets.
|
[
"cs.LG",
"cond-mat.dis-nn",
"stat.ML"
] | false |
2305.11111
|
2023-05-18T16:53:35Z
|
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State
Solutions in Disk-Planet Systems
|
[
"Shunyuan Mao",
"Ruobing Dong",
"Lu Lu",
"Kwang Moo Yi",
"Sifan Wang",
"Paris Perdikaris"
] |
We develop a tool, which we name Protoplanetary Disk Operator Network
(PPDONet), that can predict the solution of disk-planet interactions in
protoplanetary disks in real-time. We base our tool on Deep Operator Networks
(DeepONets), a class of neural networks capable of learning non-linear
operators to represent deterministic and stochastic differential equations.
With PPDONet we map three scalar parameters in a disk-planet system -- the
Shakura \& Sunyaev viscosity $\alpha$, the disk aspect ratio $h_\mathrm{0}$,
and the planet-star mass ratio $q$ -- to steady-state solutions of the disk
surface density, radial velocity, and azimuthal velocity. We demonstrate the
accuracy of the PPDONet solutions using a comprehensive set of tests. Our tool
is able to predict the outcome of disk-planet interaction for one system in
less than a second on a laptop. A public implementation of PPDONet is available
at \url{https://github.com/smao-astro/PPDONet}.
|
[
"astro-ph.EP",
"astro-ph.IM",
"cs.LG"
] | false |
2305.11135
|
2023-05-18T17:30:27Z
|
Convergence Analysis of Over-the-Air FL with Compression and Power
Control via Clipping
|
[
"Haifeng Wen",
"Hong Xing",
"Osvaldo Simeone"
] |
One of the key challenges towards the deployment of over-the-air federated
learning (AirFL) is the design of mechanisms that can comply with the power and
bandwidth constraints of the shared channel, while causing minimum
deterioration to the learning performance as compared to baseline noiseless
implementations. For additive white Gaussian noise (AWGN) channels with
instantaneous per-device power constraints, prior work has demonstrated the
optimality of a power control mechanism based on norm clipping. This was done
through the minimization of an upper bound on the optimality gap for smooth
learning objectives satisfying the Polyak-{\L}ojasiewicz (PL) condition. In
this paper, we make two contributions to the development of AirFL based on norm
clipping, which we refer to as AirFL-Clip. First, we provide a convergence
bound for AirFLClip that applies to general smooth and non-convex learning
objectives. Unlike existing results, the derived bound is free from
run-specific parameters, thus supporting an offline evaluation. Second, we
extend AirFL-Clip to include Top-k sparsification and linear compression. For
this generalized protocol, referred to as AirFL-Clip-Comp, we derive a
convergence bound for general smooth and non-convex learning objectives. We
argue, and demonstrate via experiments, that the only time-varying quantities
present in the bound can be efficiently estimated offline by leveraging the
well-studied properties of sparse recovery algorithms.
|
[
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | false |
2305.11189
|
2023-05-18T02:30:24Z
|
Taxonomy of AISecOps Threat Modeling for Cloud Based Medical Chatbots
|
[
"Ruby Annette J",
"Aisha Banu",
"Sharon Priya S",
"Subash Chandran"
] |
Artificial Intelligence (AI) is playing a vital role in all aspects of
technology including cyber security. Application of Conversational AI like the
chatbots are also becoming very popular in the medical field to provide timely
and immediate medical assistance to patients in need. As medical chatbots deal
with a lot of sensitive information, the security of these chatbots is crucial.
To secure the confidentiality, integrity, and availability of cloud-hosted
assets like these, medical chatbots can be monitored using AISecOps (Artificial
Intelligence for Secure IT Operations). AISecOPs is an emerging field that
integrates three different but interrelated domains like the IT operation, AI,
and security as one domain, where the expertise from all these three domains
are used cohesively to secure the cyber assets. It considers cloud operations
and security in a holistic framework to collect the metrics required to assess
the security threats and train the AI models to take immediate actions. This
work is focused on applying the STRIDE threat modeling framework to model the
possible threats involved in each component of the chatbot to enable the
automatic threat detection using the AISecOps techniques. This threat modeling
framework is tailored to the medical chatbots that involves sensitive data
sharing but could also be applied for chatbots used in other sectors like the
financial services, public sector, and government sectors that are concerned
with security and compliance.
|
[
"cs.DC",
"cs.AI",
"cs.HC",
"cs.LG"
] | false |
2305.11194
|
2023-05-18T13:36:57Z
|
Vaxformer: Antigenicity-controlled Transformer for Vaccine Design
Against SARS-CoV-2
|
[
"Aryo Pradipta Gema",
"Michał Kobiela",
"Achille Fraisse",
"Ajitha Rajan",
"Diego A. Oyarzún",
"Javier Antonio Alfaro"
] |
The SARS-CoV-2 pandemic has emphasised the importance of developing a
universal vaccine that can protect against current and future variants of the
virus. The present study proposes a novel conditional protein Language Model
architecture, called Vaxformer, which is designed to produce natural-looking
antigenicity-controlled SARS-CoV-2 spike proteins. We evaluate the generated
protein sequences of the Vaxformer model using DDGun protein stability measure,
netMHCpan antigenicity score, and a structure fidelity score with AlphaFold to
gauge its viability for vaccine development. Our results show that Vaxformer
outperforms the existing state-of-the-art Conditional Variational Autoencoder
model to generate antigenicity-controlled SARS-CoV-2 spike proteins. These
findings suggest promising opportunities for conditional Transformer models to
expand our understanding of vaccine design and their role in mitigating global
health challenges. The code used in this study is available at
https://github.com/aryopg/vaxformer .
|
[
"q-bio.BM",
"cs.LG",
"q-bio.QM"
] | false |
2305.11236
|
2023-05-18T18:08:36Z
|
Efficient Vertical Federated Learning with Secure Aggregation
|
[
"Xinchi Qiu",
"Heng Pan",
"Wanru Zhao",
"Chenyang Ma",
"Pedro Porto Buarque de Gusmão",
"Nicholas D. Lane"
] |
The majority of work in privacy-preserving federated learning (FL) has been
focusing on horizontally partitioned datasets where clients share the same sets
of features and can train complete models independently. However, in many
interesting problems, such as financial fraud detection and disease detection,
individual data points are scattered across different clients/organizations in
vertical federated learning. Solutions for this type of FL require the exchange
of gradients between participants and rarely consider privacy and security
concerns, posing a potential risk of privacy leakage. In this work, we present
a novel design for training vertical FL securely and efficiently using
state-of-the-art security modules for secure aggregation. We demonstrate
empirically that our method does not impact training performance whilst
obtaining 9.1e2 ~3.8e4 speedup compared to homomorphic encryption (HE).
|
[
"cs.LG",
"cs.AI",
"cs.CR"
] | false |
2305.11252
|
2023-05-18T18:34:29Z
|
Brain-inspired learning in artificial neural networks: a review
|
[
"Samuel Schmidgall",
"Jascha Achterberg",
"Thomas Miconi",
"Louis Kirsch",
"Rojin Ziaei",
"S. Pardis Hajiseyedrazi",
"Jason Eshraghian"
] |
Artificial neural networks (ANNs) have emerged as an essential tool in
machine learning, achieving remarkable success across diverse domains,
including image and speech generation, game playing, and robotics. However,
there exist fundamental differences between ANNs' operating mechanisms and
those of the biological brain, particularly concerning learning processes. This
paper presents a comprehensive review of current brain-inspired learning
representations in artificial neural networks. We investigate the integration
of more biologically plausible mechanisms, such as synaptic plasticity, to
enhance these networks' capabilities. Moreover, we delve into the potential
advantages and challenges accompanying this approach. Ultimately, we pinpoint
promising avenues for future research in this rapidly advancing field, which
could bring us closer to understanding the essence of intelligence.
|
[
"cs.NE",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | false |
2305.11278
|
2023-05-18T19:52:46Z
|
Real-Time Variational Method for Learning Neural Trajectory and its
Dynamics
|
[
"Matthew Dowling",
"Yuan Zhao",
"Il Memming Park"
] |
Latent variable models have become instrumental in computational neuroscience
for reasoning about neural computation. This has fostered the development of
powerful offline algorithms for extracting latent neural trajectories from
neural recordings. However, despite the potential of real time alternatives to
give immediate feedback to experimentalists, and enhance experimental design,
they have received markedly less attention. In this work, we introduce the
exponential family variational Kalman filter (eVKF), an online recursive
Bayesian method aimed at inferring latent trajectories while simultaneously
learning the dynamical system generating them. eVKF works for arbitrary
likelihoods and utilizes the constant base measure exponential family to model
the latent state stochasticity. We derive a closed-form variational analogue to
the predict step of the Kalman filter which leads to a provably tighter bound
on the ELBO compared to another online variational method. We validate our
method on synthetic and real-world data, and, notably, show that it achieves
competitive performance
|
[
"stat.ML",
"cs.LG",
"q-bio.NC"
] | false |
2305.11292
|
2023-05-18T20:21:22Z
|
Multi-Fidelity Machine Learning for Excited State Energies of Molecules
|
[
"Vivin Vinod",
"Sayan Maity",
"Peter Zaspel",
"Ulrich Kleinekathöfer"
] |
The accurate but fast calculation of molecular excited states is still a very
challenging topic. For many applications, detailed knowledge of the energy
funnel in larger molecular aggregates is of key importance requiring highly
accurate excited state energies. To this end, machine learning techniques can
be an extremely useful tool though the cost of generating highly accurate
training datasets still remains a severe challenge. To overcome this hurdle,
this work proposes the use of multi-fidelity machine learning where very little
training data from high accuracies is combined with cheaper and less accurate
data to achieve the accuracy of the costlier level. In the present study, the
approach is employed to predict the first excited state energies for three
molecules of increasing size, namely, benzene, naphthalene, and anthracene. The
energies are trained and tested for conformations stemming from classical
molecular dynamics simulations and from real-time density functional
tight-binding calculations. It can be shown that the multi-fidelity machine
learning model can achieve the same accuracy as a machine learning model built
only on high cost training data while having a much lower computational effort
to generate the data. The numerical gain observed in these benchmark test
calculations was over a factor of 30 but certainly can be much higher for high
accuracy data.
|
[
"physics.chem-ph",
"cs.LG",
"physics.comp-ph"
] | false |
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