arxiv_id
stringlengths 10
10
| published
stringlengths 20
20
| titles
stringlengths 9
243
| authors
listlengths 1
389
| abstract
stringlengths 96
3.09k
| categories
listlengths 1
10
| selected
bool 2
classes |
|---|---|---|---|---|---|---|
2305.17448
|
2023-05-27T11:21:32Z
|
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain
Dataset For Aspect Sentiment Triplet Extraction
|
[
"Ting Xu",
"Huiyun Yang",
"Zhen Wu",
"Jiaze Chen",
"Fei Zhao",
"Xinyu Dai"
] |
Aspect Sentiment Triplet Extraction (ASTE) is widely used in various
applications. However, existing ASTE datasets are limited in their ability to
represent real-world scenarios, hindering the advancement of research in this
area. In this paper, we introduce a new dataset, named DMASTE, which is
manually annotated to better fit real-world scenarios by providing more diverse
and realistic reviews for the task. The dataset includes various lengths,
diverse expressions, more aspect types, and more domains than existing
datasets. We conduct extensive experiments on DMASTE in multiple settings to
evaluate previous ASTE approaches. Empirical results demonstrate that DMASTE is
a more challenging ASTE dataset. Further analyses of in-domain and cross-domain
settings provide promising directions for future research. Our code and dataset
are available at https://github.com/NJUNLP/DMASTE.
|
[
"cs.CL"
] | false |
2305.17458
|
2023-05-27T12:19:21Z
|
A Diffusion Model for Event Skeleton Generation
|
[
"Fangqi Zhu",
"Lin Zhang",
"Jun Gao",
"Bing Qin",
"Ruifeng Xu",
"Haiqin Yang"
] |
Event skeleton generation, aiming to induce an event schema skeleton graph
with abstracted event nodes and their temporal relations from a set of event
instance graphs, is a critical step in the temporal complex event schema
induction task. Existing methods effectively address this task from a graph
generation perspective but suffer from noise-sensitive and error accumulation,
e.g., the inability to correct errors while generating schema. We, therefore,
propose a novel Diffusion Event Graph Model~(DEGM) to address these issues. Our
DEGM is the first workable diffusion model for event skeleton generation, where
the embedding and rounding techniques with a custom edge-based loss are
introduced to transform a discrete event graph into learnable latent
representation. Furthermore, we propose a denoising training process to
maintain the model's robustness. Consequently, DEGM derives the final schema,
where error correction is guaranteed by iteratively refining the latent
representation during the schema generation process. Experimental results on
three IED bombing datasets demonstrate that our DEGM achieves better results
than other state-of-the-art baselines. Our code and data are available at
https://github.com/zhufq00/EventSkeletonGeneration.
|
[
"cs.CL"
] | false |
2305.17491
|
2023-05-27T15:00:45Z
|
FERMAT: An Alternative to Accuracy for Numerical Reasoning
|
[
"Jasivan Alex Sivakumar",
"Nafise Sadat Moosavi"
] |
While pre-trained language models achieve impressive performance on various
NLP benchmarks, they still struggle with tasks that require numerical
reasoning. Recent advances in improving numerical reasoning are mostly achieved
using very large language models that contain billions of parameters and are
not accessible to everyone. In addition, numerical reasoning is measured using
a single score on existing datasets. As a result, we do not have a clear
understanding of the strengths and shortcomings of existing models on different
numerical reasoning aspects and therefore, potential ways to improve them apart
from scaling them up. Inspired by CheckList (Ribeiro et al., 2020), we
introduce a multi-view evaluation set for numerical reasoning in English,
called FERMAT. Instead of reporting a single score on a whole dataset, FERMAT
evaluates models on various key numerical reasoning aspects such as number
understanding, mathematical operations, and training dependency. Apart from
providing a comprehensive evaluation of models on different numerical reasoning
aspects, FERMAT enables a systematic and automated generation of an arbitrarily
large training or evaluation set for each aspect.The datasets and codes are
publicly available to generate further multi-view data for ulterior tasks and
languages.
|
[
"cs.CL"
] | false |
2305.17529
|
2023-05-27T17:09:25Z
|
MeetingBank: A Benchmark Dataset for Meeting Summarization
|
[
"Yebowen Hu",
"Tim Ganter",
"Hanieh Deilamsalehy",
"Franck Dernoncourt",
"Hassan Foroosh",
"Fei Liu"
] |
As the number of recorded meetings increases, it becomes increasingly
important to utilize summarization technology to create useful summaries of
these recordings. However, there is a crucial lack of annotated meeting corpora
for developing this technology, as it can be hard to collect meetings,
especially when the topics discussed are confidential. Furthermore, meeting
summaries written by experienced writers are scarce, making it hard for
abstractive summarizers to produce sensible output without a reliable
reference. This lack of annotated corpora has hindered the development of
meeting summarization technology. In this paper, we present MeetingBank, a new
benchmark dataset of city council meetings over the past decade. MeetingBank is
unique among other meeting corpora due to its divide-and-conquer approach,
which involves dividing professionally written meeting minutes into shorter
passages and aligning them with specific segments of the meeting. This breaks
down the process of summarizing a lengthy meeting into smaller, more manageable
tasks. The dataset provides a new testbed of various meeting summarization
systems and also allows the public to gain insight into how council decisions
are made. We make the collection, including meeting video links, transcripts,
reference summaries, agenda, and other metadata, publicly available to
facilitate the development of better meeting summarization techniques. Our
dataset can be accessed at: https://meetingbank.github.io
|
[
"cs.CL"
] | false |
2305.17561
|
2023-05-27T19:31:41Z
|
Grounding Characters and Places in Narrative Texts
|
[
"Sandeep Soni",
"Amanpreet Sihra",
"Elizabeth F. Evans",
"Matthew Wilkens",
"David Bamman"
] |
Tracking characters and locations throughout a story can help improve the
understanding of its plot structure. Prior research has analyzed characters and
locations from text independently without grounding characters to their
locations in narrative time. Here, we address this gap by proposing a new
spatial relationship categorization task. The objective of the task is to
assign a spatial relationship category for every character and location
co-mention within a window of text, taking into consideration linguistic
context, narrative tense, and temporal scope. To this end, we annotate spatial
relationships in approximately 2500 book excerpts and train a model using
contextual embeddings as features to predict these relationships. When applied
to a set of books, this model allows us to test several hypotheses on mobility
and domestic space, revealing that protagonists are more mobile than
non-central characters and that women as characters tend to occupy more
interior space than men. Overall, our work is the first step towards joint
modeling and analysis of characters and places in narrative text.
|
[
"cs.CL"
] | false |
2305.17580
|
2023-05-27T21:04:26Z
|
ArPanEmo: An Open-Source Dataset for Fine-Grained Emotion Recognition in
Arabic Online Content during COVID-19 Pandemic
|
[
"Maha Jarallah Althobaiti"
] |
Emotion recognition is a crucial task in Natural Language Processing (NLP)
that enables machines to comprehend the feelings conveyed in the text. The
applications of emotion recognition are diverse, including mental health
diagnosis, student support, and the detection of online suspicious behavior.
Despite the substantial amount of literature available on emotion recognition
in various languages, Arabic emotion recognition has received relatively little
attention, leading to a scarcity of emotion-annotated corpora. This paper
presents the ArPanEmo dataset, a novel dataset for fine-grained emotion
recognition of online posts in Arabic. The dataset comprises 11,128 online
posts manually labeled for ten emotion categories or neutral, with Fleiss'
kappa of 0.71. It targets a specific Arabic dialect and addresses topics
related to the COVID-19 pandemic, making it the first and largest of its kind.
Python's packages were utilized to collect online posts related to the COVID-19
pandemic from three sources: Twitter, YouTube, and online newspaper comments
between March 2020 and March 2022. Upon collection of the online posts, each
one underwent a semi-automatic classification process using a lexicon of
emotion-related terms to determine whether it belonged to the neutral or
emotional category. Subsequently, manual labeling was conducted to further
categorize the emotional data into fine-grained emotion categories.
|
[
"cs.CL"
] | false |
2306.00005
|
2023-05-27T17:25:13Z
|
A Two-Stage Decoder for Efficient ICD Coding
|
[
"Thanh-Tung Nguyen",
"Viktor Schlegel",
"Abhinav Kashyap",
"Stefan Winkler"
] |
Clinical notes in healthcare facilities are tagged with the International
Classification of Diseases (ICD) code; a list of classification codes for
medical diagnoses and procedures. ICD coding is a challenging multilabel text
classification problem due to noisy clinical document inputs and long-tailed
label distribution. Recent automated ICD coding efforts improve performance by
encoding medical notes and codes with additional data and knowledge bases.
However, most of them do not reflect how human coders generate the code: first,
the coders select general code categories and then look for specific
subcategories that are relevant to a patient's condition. Inspired by this, we
propose a two-stage decoding mechanism to predict ICD codes. Our model uses the
hierarchical properties of the codes to split the prediction into two steps: At
first, we predict the parent code and then predict the child code based on the
previous prediction. Experiments on the public MIMIC-III data set show that our
model performs well in single-model settings without external data or
knowledge.
|
[
"cs.CL"
] | false |
2305.17331
|
2023-05-27T02:26:52Z
|
Augmentation-Adapted Retriever Improves Generalization of Language
Models as Generic Plug-In
|
[
"Zichun Yu",
"Chenyan Xiong",
"Shi Yu",
"Zhiyuan Liu"
] |
Retrieval augmentation can aid language models (LMs) in knowledge-intensive
tasks by supplying them with external information. Prior works on retrieval
augmentation usually jointly fine-tune the retriever and the LM, making them
closely coupled. In this paper, we explore the scheme of generic retrieval
plug-in: the retriever is to assist target LMs that may not be known beforehand
or are unable to be fine-tuned together. To retrieve useful documents for
unseen target LMs, we propose augmentation-adapted retriever (AAR), which
learns LM's preferences obtained from a known source LM. Experiments on the
MMLU and PopQA datasets demonstrate that our AAR trained with a small source LM
is able to significantly improve the zero-shot generalization of larger target
LMs ranging from 250M Flan-T5 to 175B InstructGPT. Further analysis indicates
that the preferences of different LMs overlap, enabling AAR trained with a
single source LM to serve as a generic plug-in for various target LMs. Our code
is open-sourced at https://github.com/OpenMatch/Augmentation-Adapted-Retriever.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.17337
|
2023-05-27T02:38:46Z
|
Benchmarking Diverse-Modal Entity Linking with Generative Models
|
[
"Sijia Wang",
"Alexander Hanbo Li",
"Henry Zhu",
"Sheng Zhang",
"Chung-Wei Hang",
"Pramuditha Perera",
"Jie Ma",
"William Wang",
"Zhiguo Wang",
"Vittorio Castelli",
"Bing Xiang",
"Patrick Ng"
] |
Entities can be expressed in diverse formats, such as texts, images, or
column names and cell values in tables. While existing entity linking (EL)
models work well on per modality configuration, such as text-only EL, visual
grounding, or schema linking, it is more challenging to design a unified model
for diverse modality configurations. To bring various modality configurations
together, we constructed a benchmark for diverse-modal EL (DMEL) from existing
EL datasets, covering all three modalities including text, image, and table. To
approach the DMEL task, we proposed a generative diverse-modal model (GDMM)
following a multimodal-encoder-decoder paradigm. Pre-training \Model with rich
corpora builds a solid foundation for DMEL without storing the entire KB for
inference. Fine-tuning GDMM builds a stronger DMEL baseline, outperforming
state-of-the-art task-specific EL models by 8.51 F1 score on average.
Additionally, extensive error analyses are conducted to highlight the
challenges of DMEL, facilitating future research on this task.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.17373
|
2023-05-27T05:36:46Z
|
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning
|
[
"Zhenrui Yue",
"Huimin Zeng",
"Mengfei Lan",
"Heng Ji",
"Dong Wang"
] |
With emerging online topics as a source for numerous new events, detecting
unseen / rare event types presents an elusive challenge for existing event
detection methods, where only limited data access is provided for training. To
address the data scarcity problem in event detection, we propose MetaEvent, a
meta learning-based framework for zero- and few-shot event detection.
Specifically, we sample training tasks from existing event types and perform
meta training to search for optimal parameters that quickly adapt to unseen
tasks. In our framework, we propose to use the cloze-based prompt and a
trigger-aware soft verbalizer to efficiently project output to unseen event
types. Moreover, we design a contrastive meta objective based on maximum mean
discrepancy (MMD) to learn class-separating features. As such, the proposed
MetaEvent can perform zero-shot event detection by mapping features to event
types without any prior knowledge. In our experiments, we demonstrate the
effectiveness of MetaEvent in both zero-shot and few-shot scenarios, where the
proposed method achieves state-of-the-art performance in extensive experiments
on benchmark datasets FewEvent and MAVEN.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.17378
|
2023-05-27T06:09:03Z
|
Improving Generalization in Language Model-Based Text-to-SQL Semantic
Parsing: Two Simple Semantic Boundary-Based Techniques
|
[
"Daking Rai",
"Bailin Wang",
"Yilun Zhou",
"Ziyu Yao"
] |
Compositional and domain generalization present significant challenges in
semantic parsing, even for state-of-the-art semantic parsers based on
pre-trained language models (LMs). In this study, we empirically investigate
improving an LM's generalization in semantic parsing with two simple
techniques: at the token level, we introduce a token preprocessing method to
preserve the semantic boundaries of tokens produced by LM tokenizers; at the
sequence level, we propose to use special tokens to mark the boundaries of
components aligned between input and output. Our experimental results on two
text-to-SQL semantic parsing datasets show that our token preprocessing,
although simple, can substantially improve the LM performance on both types of
generalization, and our component boundary marking method is particularly
helpful for compositional generalization.
|
[
"cs.CL",
"cs.AI",
"I.2.7"
] | false |
2305.17542
|
2023-05-27T18:13:17Z
|
Non-Sequential Graph Script Induction via Multimedia Grounding
|
[
"Yu Zhou",
"Sha Li",
"Manling Li",
"Xudong Lin",
"Shih-Fu Chang",
"Mohit Bansal",
"Heng Ji"
] |
Online resources such as WikiHow compile a wide range of scripts for
performing everyday tasks, which can assist models in learning to reason about
procedures. However, the scripts are always presented in a linear manner, which
does not reflect the flexibility displayed by people executing tasks in real
life. For example, in the CrossTask Dataset, 64.5% of consecutive step pairs
are also observed in the reverse order, suggesting their ordering is not fixed.
In addition, each step has an average of 2.56 frequent next steps,
demonstrating "branching". In this paper, we propose the new challenging task
of non-sequential graph script induction, aiming to capture optional and
interchangeable steps in procedural planning. To automate the induction of such
graph scripts for given tasks, we propose to take advantage of loosely aligned
videos of people performing the tasks. In particular, we design a multimodal
framework to ground procedural videos to WikiHow textual steps and thus
transform each video into an observed step path on the latent ground truth
graph script. This key transformation enables us to train a script knowledge
model capable of both generating explicit graph scripts for learnt tasks and
predicting future steps given a partial step sequence. Our best model
outperforms the strongest pure text/vision baselines by 17.52% absolute gains
on F1@3 for next step prediction and 13.8% absolute gains on Acc@1 for partial
sequence completion. Human evaluation shows our model outperforming the WikiHow
linear baseline by 48.76% absolute gains in capturing sequential and
non-sequential step relationships.
|
[
"cs.CL",
"cs.MM"
] | false |
2305.17311
|
2023-05-27T00:07:17Z
|
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language
Models
|
[
"Yuhui Zhang",
"Michihiro Yasunaga",
"Zhengping Zhou",
"Jeff Z. HaoChen",
"James Zou",
"Percy Liang",
"Serena Yeung"
] |
Language models have been shown to exhibit positive scaling, where
performance improves as models are scaled up in terms of size, compute, or
data. In this work, we introduce NeQA, a dataset consisting of questions with
negation in which language models do not exhibit straightforward positive
scaling. We show that this task can exhibit inverse scaling, U-shaped scaling,
or positive scaling, and the three scaling trends shift in this order as we use
more powerful prompting methods or model families. We hypothesize that solving
NeQA depends on two subtasks: question answering (task 1) and negation
understanding (task 2). We find that task 1 has linear scaling, while task 2
has sigmoid-shaped scaling with an emergent transition point, and composing
these two scaling trends yields the final scaling trend of NeQA. Our work
reveals and provides a way to analyze the complex scaling trends of language
models.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.17364
|
2023-05-27T04:34:58Z
|
An Investigation of Evaluation Metrics for Automated Medical Note
Generation
|
[
"Asma Ben Abacha",
"Wen-wai Yim",
"George Michalopoulos",
"Thomas Lin"
] |
Recent studies on automatic note generation have shown that doctors can save
significant amounts of time when using automatic clinical note generation
(Knoll et al., 2022). Summarization models have been used for this task to
generate clinical notes as summaries of doctor-patient conversations (Krishna
et al., 2021; Cai et al., 2022). However, assessing which model would best
serve clinicians in their daily practice is still a challenging task due to the
large set of possible correct summaries, and the potential limitations of
automatic evaluation metrics. In this paper, we study evaluation methods and
metrics for the automatic generation of clinical notes from medical
conversations. In particular, we propose new task-specific metrics and we
compare them to SOTA evaluation metrics in text summarization and generation,
including: (i) knowledge-graph embedding-based metrics, (ii) customized
model-based metrics, (iii) domain-adapted/fine-tuned metrics, and (iv) ensemble
metrics. To study the correlation between the automatic metrics and manual
judgments, we evaluate automatic notes/summaries by comparing the system and
reference facts and computing the factual correctness, and the hallucination
and omission rates for critical medical facts. This study relied on seven
datasets manually annotated by domain experts. Our experiments show that
automatic evaluation metrics can have substantially different behaviors on
different types of clinical notes datasets. However, the results highlight one
stable subset of metrics as the most correlated with human judgments with a
relevant aggregation of different evaluation criteria.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.17444
|
2023-05-27T11:00:15Z
|
Query-Efficient Black-Box Red Teaming via Bayesian Optimization
|
[
"Deokjae Lee",
"JunYeong Lee",
"Jung-Woo Ha",
"Jin-Hwa Kim",
"Sang-Woo Lee",
"Hwaran Lee",
"Hyun Oh Song"
] |
The deployment of large-scale generative models is often restricted by their
potential risk of causing harm to users in unpredictable ways. We focus on the
problem of black-box red teaming, where a red team generates test cases and
interacts with the victim model to discover a diverse set of failures with
limited query access. Existing red teaming methods construct test cases based
on human supervision or language model (LM) and query all test cases in a
brute-force manner without incorporating any information from past evaluations,
resulting in a prohibitively large number of queries. To this end, we propose
Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods
based on Bayesian optimization, which iteratively identify diverse positive
test cases leading to model failures by utilizing the pre-defined user input
pool and the past evaluations. Experimental results on various user input pools
demonstrate that our method consistently finds a significantly larger number of
diverse positive test cases under the limited query budget than the baseline
methods. The source code is available at
https://github.com/snu-mllab/Bayesian-Red-Teaming.
|
[
"cs.AI",
"cs.CL",
"cs.CR",
"cs.LG"
] | false |
2305.17457
|
2023-05-27T12:19:13Z
|
Financial misstatement detection: a realistic evaluation
|
[
"Elias Zavitsanos",
"Dimitris Mavroeidis",
"Konstantinos Bougiatiotis",
"Eirini Spyropoulou",
"Lefteris Loukas",
"Georgios Paliouras"
] |
In this work, we examine the evaluation process for the task of detecting
financial reports with a high risk of containing a misstatement. This task is
often referred to, in the literature, as ``misstatement detection in financial
reports''. We provide an extensive review of the related literature. We propose
a new, realistic evaluation framework for the task which, unlike a large part
of the previous work: (a) focuses on the misstatement class and its rarity, (b)
considers the dimension of time when splitting data into training and test and
(c) considers the fact that misstatements can take a long time to detect. Most
importantly, we show that the evaluation process significantly affects system
performance, and we analyze the performance of different models and feature
types in the new realistic framework.
|
[
"cs.CL",
"cs.LG",
"q-fin.CP"
] | false |
2305.17499
|
2023-05-27T15:39:13Z
|
CIF-PT: Bridging Speech and Text Representations for Spoken Language
Understanding via Continuous Integrate-and-Fire Pre-Training
|
[
"Linhao Dong",
"Zhecheng An",
"Peihao Wu",
"Jun Zhang",
"Lu Lu",
"Zejun Ma"
] |
Speech or text representation generated by pre-trained models contains
modal-specific information that could be combined for benefiting spoken
language understanding (SLU) tasks. In this work, we propose a novel
pre-training paradigm termed Continuous Integrate-and-Fire Pre-Training
(CIF-PT). It relies on a simple but effective frame-to-token alignment:
continuous integrate-and-fire (CIF) to bridge the representations between
speech and text. It jointly performs speech-to-text training and language model
distillation through CIF as the pre-training (PT). Evaluated on SLU benchmark
SLURP dataset, CIF-PT outperforms the state-of-the-art model by 1.94% of
accuracy and 2.71% of SLU-F1 on the tasks of intent classification and slot
filling, respectively. We also observe the cross-modal representation extracted
by CIF-PT obtains better performance than other neural interfaces for the tasks
of SLU, including the dominant speech representation learned from
self-supervised pre-training.
|
[
"cs.CL",
"cs.MM",
"eess.AS"
] | false |
2305.17534
|
2023-05-27T17:34:36Z
|
Unsupervised Selective Rationalization with Noise Injection
|
[
"Adam Storek",
"Melanie Subbiah",
"Kathleen McKeown"
] |
A major issue with using deep learning models in sensitive applications is
that they provide no explanation for their output. To address this problem,
unsupervised selective rationalization produces rationales alongside
predictions by chaining two jointly-trained components, a rationale generator
and a predictor. Although this architecture guarantees that the prediction
relies solely on the rationale, it does not ensure that the rationale contains
a plausible explanation for the prediction. We introduce a novel training
technique that effectively limits generation of implausible rationales by
injecting noise between the generator and the predictor. Furthermore, we
propose a new benchmark for evaluating unsupervised selective rationalization
models using movie reviews from existing datasets. We achieve sizeable
improvements in rationale plausibility and task accuracy over the
state-of-the-art across a variety of tasks, including our new benchmark, while
maintaining or improving model faithfulness.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.17315
|
2023-05-27T00:36:30Z
|
Automatic Roof Type Classification Through Machine Learning for Regional
Wind Risk Assessment
|
[
"Shuochuan Meng",
"Mohammad Hesam Soleimani-Babakamali",
"Ertugrul Taciroglu"
] |
Roof type is one of the most critical building characteristics for wind
vulnerability modeling. It is also the most frequently missing building feature
from publicly available databases. An automatic roof classification framework
is developed herein to generate high-resolution roof-type data using machine
learning. A Convolutional Neural Network (CNN) was trained to classify roof
types using building-level satellite images. The model achieved an F1 score of
0.96 on predicting roof types for 1,000 test buildings. The CNN model was then
used to predict roof types for 161,772 single-family houses in New Hanover
County, NC, and Miami-Dade County, FL. The distribution of roof type in city
and census tract scales was presented. A high variance was observed in the
dominant roof type among census tracts. To improve the completeness of the
roof-type data, imputation algorithms were developed to populate missing roof
data due to low-quality images, using critical building attributes and
neighborhood-level roof characteristics.
|
[
"cs.LG"
] | false |
2305.17409
|
2023-05-27T08:22:34Z
|
On the special role of class-selective neurons in early training
|
[
"Omkar Ranadive",
"Nikhil Thakurdesai",
"Ari S Morcos",
"Matthew Leavitt",
"Stéphane Deny"
] |
It is commonly observed that deep networks trained for classification exhibit
class-selective neurons in their early and intermediate layers. Intriguingly,
recent studies have shown that these class-selective neurons can be ablated
without deteriorating network function. But if class-selective neurons are not
necessary, why do they exist? We attempt to answer this question in a series of
experiments on ResNet-50s trained on ImageNet. We first show that
class-selective neurons emerge during the first few epochs of training, before
receding rapidly but not completely; this suggests that class-selective neurons
found in trained networks are in fact vestigial remains of early training. With
single-neuron ablation experiments, we then show that class-selective neurons
are important for network function in this early phase of training. We also
observe that the network is close to a linear regime in this early phase; we
thus speculate that class-selective neurons appear early in training as
quasi-linear shortcut solutions to the classification task. Finally, in causal
experiments where we regularize against class selectivity at different points
in training, we show that the presence of class-selective neurons early in
training is critical to the successful training of the network; in contrast,
class-selective neurons can be suppressed later in training with little effect
on final accuracy. It remains to be understood by which mechanism the presence
of class-selective neurons in the early phase of training contributes to the
successful training of networks.
|
[
"cs.LG"
] | false |
2305.17428
|
2023-05-27T09:37:58Z
|
Choosing the Right Weights: Balancing Value, Strategy, and Noise in
Recommender Systems
|
[
"Smitha Milli",
"Emma Pierson",
"Nikhil Garg"
] |
Many recommender systems are based on optimizing a linear weighting of
different user behaviors, such as clicks, likes, shares, etc. Though the choice
of weights can have a significant impact, there is little formal study or
guidance on how to choose them. We analyze the optimal choice of weights from
the perspectives of both users and content producers who strategically respond
to the weights. We consider three aspects of user behavior: value-faithfulness
(how well a behavior indicates whether the user values the content),
strategy-robustness (how hard it is for producers to manipulate the behavior),
and noisiness (how much estimation error there is in predicting the behavior).
Our theoretical results show that for users, upweighting more value-faithful
and less noisy behaviors leads to higher utility, while for producers,
upweighting more value-faithful and strategy-robust behaviors leads to higher
welfare (and the impact of noise is non-monotonic). Finally, we discuss how our
results can help system designers select weights in practice.
|
[
"cs.LG"
] | false |
2305.17492
|
2023-05-27T15:09:56Z
|
Dynamic User Segmentation and Usage Profiling
|
[
"Animesh Mitra",
"Saswata Sahoo",
"Soumyabrata Dey"
] |
Usage data of a group of users distributed across a number of categories,
such as songs, movies, webpages, links, regular household products, mobile
apps, games, etc. can be ultra-high dimensional and massive in size. More often
this kind of data is categorical and sparse in nature making it even more
difficult to interpret any underlying hidden patterns such as clusters of
users. However, if this information can be estimated accurately, it will have
huge impacts in different business areas such as user recommendations for apps,
songs, movies, and other similar products, health analytics using electronic
health record (EHR) data, and driver profiling for insurance premium estimation
or fleet management.
In this work, we propose a clustering strategy of such categorical big data,
utilizing the hidden sparsity of the dataset. Most traditional clustering
methods fail to give proper clusters for such data and end up giving one big
cluster with small clusters around it irrespective of the true structure of the
data clusters. We propose a feature transformation, which maps the
binary-valued usage vector to a lower dimensional continuous feature space in
terms of groups of usage categories, termed as covariate classes. The lower
dimensional feature representations in terms of covariate classes can be used
for clustering. We implemented the proposed strategy and applied it to a large
sized very high-dimensional song playlist dataset for the performance
validation. The results are impressive as we achieved similar-sized user
clusters with minimal between-cluster overlap in the feature space (8%) on
average). As the proposed strategy has a very generic framework, it can be
utilized as the analytic engine of many of the above-mentioned business use
cases allowing an intelligent and dynamic personal recommendation system or a
support system for smart business decision-making.
|
[
"cs.LG"
] | false |
2305.17528
|
2023-05-27T17:06:17Z
|
Two Heads are Better than One: Towards Better Adversarial Robustness by
Combining Transduction and Rejection
|
[
"Nils Palumbo",
"Yang Guo",
"Xi Wu",
"Jiefeng Chen",
"Yingyu Liang",
"Somesh Jha"
] |
Both transduction and rejection have emerged as important techniques for
defending against adversarial perturbations. A recent work by Tram\`er showed
that, in the rejection-only case (no transduction), a strong rejection-solution
can be turned into a strong (but computationally inefficient) non-rejection
solution. This detector-to-classifier reduction has been mostly applied to give
evidence that certain claims of strong selective-model solutions are
susceptible, leaving the benefits of rejection unclear. On the other hand, a
recent work by Goldwasser et al. showed that rejection combined with
transduction can give provable guarantees (for certain problems) that cannot be
achieved otherwise. Nevertheless, under recent strong adversarial attacks
(GMSA, which has been shown to be much more effective than AutoAttack against
transduction), Goldwasser et al.'s work was shown to have low performance in a
practical deep-learning setting. In this paper, we take a step towards
realizing the promise of transduction+rejection in more realistic scenarios.
Theoretically, we show that a novel application of Tram\`er's
classifier-to-detector technique in the transductive setting can give
significantly improved sample-complexity for robust generalization. While our
theoretical construction is computationally inefficient, it guides us to
identify an efficient transductive algorithm to learn a selective model.
Extensive experiments using state of the art attacks (AutoAttack, GMSA) show
that our solutions provide significantly better robust accuracy.
|
[
"cs.LG"
] | false |
2305.17346
|
2023-05-27T03:01:27Z
|
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient
In-Memory Computing
|
[
"Yuhang Li",
"Abhishek Moitra",
"Tamar Geller",
"Priyadarshini Panda"
] |
Spiking Neural Networks (SNNs) have recently attracted widespread research
interest as an efficient alternative to traditional Artificial Neural Networks
(ANNs) because of their capability to process sparse and binary spike
information and avoid expensive multiplication operations. Although the
efficiency of SNNs can be realized on the In-Memory Computing (IMC)
architecture, we show that the energy cost and latency of SNNs scale linearly
with the number of timesteps used on IMC hardware. Therefore, in order to
maximize the efficiency of SNNs, we propose input-aware Dynamic Timestep SNN
(DT-SNN), a novel algorithmic solution to dynamically determine the number of
timesteps during inference on an input-dependent basis. By calculating the
entropy of the accumulated output after each timestep, we can compare it to a
predefined threshold and decide if the information processed at the current
timestep is sufficient for a confident prediction. We deploy DT-SNN on an IMC
architecture and show that it incurs negligible computational overhead. We
demonstrate that our method only uses 1.46 average timesteps to achieve the
accuracy of a 4-timestep static SNN while reducing the energy-delay-product by
80%.
|
[
"cs.NE",
"cs.LG"
] | false |
2305.17386
|
2023-05-27T06:35:23Z
|
HyperFormer: Learning Expressive Sparse Feature Representations via
Hypergraph Transformer
|
[
"Kaize Ding",
"Albert Jiongqian Liang",
"Bryan Perrozi",
"Ting Chen",
"Ruoxi Wang",
"Lichan Hong",
"Ed H. Chi",
"Huan Liu",
"Derek Zhiyuan Cheng"
] |
Learning expressive representations for high-dimensional yet sparse features
has been a longstanding problem in information retrieval. Though recent deep
learning methods can partially solve the problem, they often fail to handle the
numerous sparse features, particularly those tail feature values with
infrequent occurrences in the training data. Worse still, existing methods
cannot explicitly leverage the correlations among different instances to help
further improve the representation learning on sparse features since such
relational prior knowledge is not provided. To address these challenges, in
this paper, we tackle the problem of representation learning on feature-sparse
data from a graph learning perspective. Specifically, we propose to model the
sparse features of different instances using hypergraphs where each node
represents a data instance and each hyperedge denotes a distinct feature value.
By passing messages on the constructed hypergraphs based on our Hypergraph
Transformer (HyperFormer), the learned feature representations capture not only
the correlations among different instances but also the correlations among
features. Our experiments demonstrate that the proposed approach can
effectively improve feature representation learning on sparse features.
|
[
"cs.IR",
"cs.LG"
] | false |
2305.17408
|
2023-05-27T08:22:12Z
|
AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels
on GPUs
|
[
"Yangjie Zhou",
"Yaoxu Song",
"Jingwen Leng",
"Zihan Liu",
"Weihao Cui",
"Zhendong Zhang",
"Cong Guo",
"Quan Chen",
"Li Li",
"Minyi Guo"
] |
Graph neural networks (GNNs) are powerful tools for exploring and learning
from graph structures and features. As such, achieving high-performance
execution for GNNs becomes crucially important. Prior works have proposed to
explore the sparsity (i.e., low density) in the input graph to accelerate GNNs,
which uses the full-graph-level or block-level sparsity format. We show that
they fail to balance the sparsity benefit and kernel execution efficiency. In
this paper, we propose a novel system, referred to as AdaptGear, that addresses
the challenge of optimizing GNNs performance by leveraging kernels tailored to
the density characteristics at the subgraph level. Meanwhile, we also propose a
method that dynamically chooses the optimal set of kernels for a given input
graph. Our evaluation shows that AdaptGear can achieve a significant
performance improvement, up to $6.49 \times$ ($1.87 \times$ on average), over
the state-of-the-art works on two mainstream NVIDIA GPUs across various
datasets.
|
[
"cs.DC",
"cs.LG"
] | false |
2305.17437
|
2023-05-27T10:24:22Z
|
GIMM: InfoMin-Max for Automated Graph Contrastive Learning
|
[
"Xin Xiong",
"Furao Shen",
"Xiangyu Wang",
"Jian Zhao"
] |
Graph contrastive learning (GCL) shows great potential in unsupervised graph
representation learning. Data augmentation plays a vital role in GCL, and its
optimal choice heavily depends on the downstream task. Many GCL methods with
automated data augmentation face the risk of insufficient information as they
fail to preserve the essential information necessary for the downstream task.
To solve this problem, we propose InfoMin-Max for automated Graph contrastive
learning (GIMM), which prevents GCL from encoding redundant information and
losing essential information. GIMM consists of two major modules: (1) automated
graph view generator, which acquires the approximation of InfoMin's optimal
views through adversarial training without requiring task-relevant information;
(2) view comparison, which learns an excellent encoder by applying InfoMax to
view representations. To the best of our knowledge, GIMM is the first method
that combines the InfoMin and InfoMax principles in GCL. Besides, GIMM
introduces randomness to augmentation, thus stabilizing the model against
perturbations. Extensive experiments on unsupervised and semi-supervised
learning for node and graph classification demonstrate the superiority of our
GIMM over state-of-the-art GCL methods with automated and manual data
augmentation.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.17476
|
2023-05-27T13:46:08Z
|
Toward Understanding Generative Data Augmentation
|
[
"Chenyu Zheng",
"Guoqiang Wu",
"Chongxuan Li"
] |
Generative data augmentation, which scales datasets by obtaining fake labeled
examples from a trained conditional generative model, boosts classification
performance in various learning tasks including (semi-)supervised learning,
few-shot learning, and adversarially robust learning. However, little work has
theoretically investigated the effect of generative data augmentation. To fill
this gap, we establish a general stability bound in this not independently and
identically distributed (non-i.i.d.) setting, where the learned distribution is
dependent on the original train set and generally not the same as the true
distribution. Our theoretical result includes the divergence between the
learned distribution and the true distribution. It shows that generative data
augmentation can enjoy a faster learning rate when the order of divergence term
is $o(\max\left( \log(m)\beta_m, 1 / \sqrt{m})\right)$, where $m$ is the train
set size and $\beta_m$ is the corresponding stability constant. We further
specify the learning setup to the Gaussian mixture model and generative
adversarial nets. We prove that in both cases, though generative data
augmentation does not enjoy a faster learning rate, it can improve the learning
guarantees at a constant level when the train set is small, which is
significant when the awful overfitting occurs. Simulation results on the
Gaussian mixture model and empirical results on generative adversarial nets
support our theoretical conclusions. Our code is available at
https://github.com/ML-GSAI/Understanding-GDA.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.17482
|
2023-05-27T14:23:14Z
|
Federated Empirical Risk Minimization via Second-Order Method
|
[
"Song Bian",
"Zhao Song",
"Junze Yin"
] |
Many convex optimization problems with important applications in machine
learning are formulated as empirical risk minimization (ERM). There are several
examples: linear and logistic regression, LASSO, kernel regression, quantile
regression, $p$-norm regression, support vector machines (SVM), and mean-field
variational inference. To improve data privacy, federated learning is proposed
in machine learning as a framework for training deep learning models on the
network edge without sharing data between participating nodes. In this work, we
present an interior point method (IPM) to solve a general ERM problem under the
federated learning setting. We show that the communication complexity of each
iteration of our IPM is $\tilde{O}(d^{3/2})$, where $d$ is the dimension (i.e.,
number of features) of the dataset.
|
[
"cs.LG",
"cs.DC"
] | false |
2305.17498
|
2023-05-27T15:38:53Z
|
A Model-Based Method for Minimizing CVaR and Beyond
|
[
"Si Yi Meng",
"Robert M. Gower"
] |
We develop a variant of the stochastic prox-linear method for minimizing the
Conditional Value-at-Risk (CVaR) objective. CVaR is a risk measure focused on
minimizing worst-case performance, defined as the average of the top quantile
of the losses. In machine learning, such a risk measure is useful to train more
robust models. Although the stochastic subgradient method (SGM) is a natural
choice for minimizing the CVaR objective, we show that our stochastic
prox-linear (SPL+) algorithm can better exploit the structure of the objective,
while still providing a convenient closed form update. Our SPL+ method also
adapts to the scaling of the loss function, which allows for easier tuning. We
then specialize a general convergence theorem for SPL+ to our setting, and show
that it allows for a wider selection of step sizes compared to SGM. We support
this theoretical finding experimentally.
|
[
"math.OC",
"cs.LG"
] | false |
2305.17523
|
2023-05-27T16:38:18Z
|
A Comparative Analysis of Portfolio Optimization Using Mean-Variance,
Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian
Stock Market
|
[
"Jaydip Sen",
"Aditya Jaiswal",
"Anshuman Pathak",
"Atish Kumar Majee",
"Kushagra Kumar",
"Manas Kumar Sarkar",
"Soubhik Maji"
] |
This paper presents a comparative analysis of the performances of three
portfolio optimization approaches. Three approaches of portfolio optimization
that are considered in this work are the mean-variance portfolio (MVP),
hierarchical risk parity (HRP) portfolio, and reinforcement learning-based
portfolio. The portfolios are trained and tested over several stock data and
their performances are compared on their annual returns, annual risks, and
Sharpe ratios. In the reinforcement learning-based portfolio design approach,
the deep Q learning technique has been utilized. Due to the large number of
possible states, the construction of the Q-table is done using a deep neural
network. The historical prices of the 50 premier stocks from the Indian stock
market, known as the NIFTY50 stocks, and several stocks from 10 important
sectors of the Indian stock market are used to create the environment for
training the agent.
|
[
"cs.LG",
"q-fin.PM"
] | false |
2305.17568
|
2023-05-27T20:08:35Z
|
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with
General Utilities
|
[
"Donghao Ying",
"Yunkai Zhang",
"Yuhao Ding",
"Alec Koppel",
"Javad Lavaei"
] |
We investigate safe multi-agent reinforcement learning, where agents seek to
collectively maximize an aggregate sum of local objectives while satisfying
their own safety constraints. The objective and constraints are described by
{\it general utilities}, i.e., nonlinear functions of the long-term
state-action occupancy measure, which encompass broader decision-making goals
such as risk, exploration, or imitations. The exponential growth of the
state-action space size with the number of agents presents challenges for
global observability, further exacerbated by the global coupling arising from
agents' safety constraints. To tackle this issue, we propose a primal-dual
method utilizing shadow reward and $\kappa$-hop neighbor truncation under a
form of correlation decay property, where $\kappa$ is the communication radius.
In the exact setting, our algorithm converges to a first-order stationary point
(FOSP) at the rate of $\mathcal{O}\left(T^{-2/3}\right)$. In the sample-based
setting, we demonstrate that, with high probability, our algorithm requires
$\widetilde{\mathcal{O}}\left(\epsilon^{-3.5}\right)$ samples to achieve an
$\epsilon$-FOSP with an approximation error of $\mathcal{O}(\phi_0^{2\kappa})$,
where $\phi_0\in (0,1)$. Finally, we demonstrate the effectiveness of our model
through extensive numerical experiments.
|
[
"cs.LG",
"math.OC"
] | false |
2305.17589
|
2023-05-27T22:26:27Z
|
Graph Inductive Biases in Transformers without Message Passing
|
[
"Liheng Ma",
"Chen Lin",
"Derek Lim",
"Adriana Romero-Soriano",
"Puneet K. Dokania",
"Mark Coates",
"Philip Torr",
"Ser-Nam Lim"
] |
Transformers for graph data are increasingly widely studied and successful in
numerous learning tasks. Graph inductive biases are crucial for Graph
Transformers, and previous works incorporate them using message-passing modules
and/or positional encodings. However, Graph Transformers that use
message-passing inherit known issues of message-passing, and differ
significantly from Transformers used in other domains, thus making transfer of
research advances more difficult. On the other hand, Graph Transformers without
message-passing often perform poorly on smaller datasets, where inductive
biases are more crucial. To bridge this gap, we propose the Graph Inductive
bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph
inductive biases without using message passing. GRIT is based on several
architectural changes that are each theoretically and empirically justified,
including: learned relative positional encodings initialized with random walk
probabilities, a flexible attention mechanism that updates node and node-pair
representations, and injection of degree information in each layer. We prove
that GRIT is expressive -- it can express shortest path distances and various
graph propagation matrices. GRIT achieves state-of-the-art empirical
performance across a variety of graph datasets, thus showing the power that
Graph Transformers without message-passing can deliver.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.17592
|
2023-05-27T22:53:37Z
|
Approximation-Generalization Trade-offs under (Approximate) Group
Equivariance
|
[
"Mircea Petrache",
"Shubhendu Trivedi"
] |
The explicit incorporation of task-specific inductive biases through symmetry
has emerged as a general design precept in the development of high-performance
machine learning models. For example, group equivariant neural networks have
demonstrated impressive performance across various domains and applications
such as protein and drug design. A prevalent intuition about such models is
that the integration of relevant symmetry results in enhanced generalization.
Moreover, it is posited that when the data and/or the model may only exhibit
$\textit{approximate}$ or $\textit{partial}$ symmetry, the optimal or
best-performing model is one where the model symmetry aligns with the data
symmetry. In this paper, we conduct a formal unified investigation of these
intuitions. To begin, we present general quantitative bounds that demonstrate
how models capturing task-specific symmetries lead to improved generalization.
In fact, our results do not require the transformations to be finite or even
form a group and can work with partial or approximate equivariance. Utilizing
this quantification, we examine the more general question of model
mis-specification i.e. when the model symmetries don't align with the data
symmetries. We establish, for a given symmetry group, a quantitative comparison
between the approximate/partial equivariance of the model and that of the data
distribution, precisely connecting model equivariance error and data
equivariance error. Our result delineates conditions under which the model
equivariance error is optimal, thereby yielding the best-performing model for
the given task and data.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.17593
|
2023-05-27T23:03:41Z
|
Data Minimization at Inference Time
|
[
"Cuong Tran",
"Ferdinando Fioretto"
] |
In domains with high stakes such as law, recruitment, and healthcare,
learning models frequently rely on sensitive user data for inference,
necessitating the complete set of features. This not only poses significant
privacy risks for individuals but also demands substantial human effort from
organizations to verify information accuracy. This paper asks whether it is
necessary to use \emph{all} input features for accurate predictions at
inference time. The paper demonstrates that, in a personalized setting,
individuals may only need to disclose a small subset of their features without
compromising decision-making accuracy. The paper also provides an efficient
sequential algorithm to determine the appropriate attributes for each
individual to provide. Evaluations across various learning tasks show that
individuals can potentially report as little as 10\% of their information while
maintaining the same accuracy level as a model that employs the full set of
user information.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.18372
|
2023-05-27T23:30:27Z
|
Assumption Generation for the Verification of Learning-Enabled
Autonomous Systems
|
[
"Corina Pasareanu",
"Ravi Mangal",
"Divya Gopinath",
"Huafeng Yu"
] |
Providing safety guarantees for autonomous systems is difficult as these
systems operate in complex environments that require the use of
learning-enabled components, such as deep neural networks (DNNs) for visual
perception. DNNs are hard to analyze due to their size (they can have thousands
or millions of parameters), lack of formal specifications (DNNs are typically
learnt from labeled data, in the absence of any formal requirements), and
sensitivity to small changes in the environment. We present an assume-guarantee
style compositional approach for the formal verification of system-level safety
properties of such autonomous systems. Our insight is that we can analyze the
system in the absence of the DNN perception components by automatically
synthesizing assumptions on the DNN behaviour that guarantee the satisfaction
of the required safety properties. The synthesized assumptions are the weakest
in the sense that they characterize the output sequences of all the possible
DNNs that, plugged into the autonomous system, guarantee the required safety
properties. The assumptions can be leveraged as run-time monitors over a
deployed DNN to guarantee the safety of the overall system; they can also be
mined to extract local specifications for use during training and testing of
DNNs. We illustrate our approach on a case study taken from the autonomous
airplanes domain that uses a complex DNN for perception.
|
[
"cs.AI",
"cs.LG"
] | false |
2307.10177
|
2023-05-27T20:12:15Z
|
Bayesian Spike Train Inference via Non-Local Priors
|
[
"Abhisek Chakraborty"
] |
Advances in neuroscience have enabled researchers to measure the activities
of large numbers of neurons simultaneously in behaving animals. We have access
to the fluorescence of each of the neurons which provides a first-order
approximation of the neural activity over time. Determining the exact spike of
a neuron from this fluorescence trace constitutes an active area of research
within the field of computational neuroscience. We propose a novel Bayesian
approach based on a mixture of half-non-local prior densities and point masses
for this task. Instead of a computationally expensive MCMC algorithm, we adopt
a stochastic search-based approach that is capable of taking advantage of
modern computing environments often equipped with multiple processors, to
explore all possible arrangements of spikes and lack thereof in an observed
spike train. It then reports the highest posterior probability arrangement of
spikes and posterior probability for a spike at each location of the spike
train. Our proposals lead to substantial improvements over existing proposals
based on L1 regularization, and enjoy comparable estimation accuracy to the
state-of-the-art L0 proposal, in simulations, and on recent calcium imaging
data sets. Notably, contrary to optimization-based frequentist approaches, our
methodology yields automatic uncertainty quantification associated with the
spike-train inference.
|
[
"q-bio.NC",
"cs.LG"
] | false |
2305.17332
|
2023-05-27T02:27:27Z
|
Learning Capacity: A Measure of the Effective Dimensionality of a Model
|
[
"Daiwei Chen",
"Weikai Chang",
"Pratik Chaudhari"
] |
We exploit a formal correspondence between thermodynamics and inference,
where the number of samples can be thought of as the inverse temperature, to
define a "learning capacity'' which is a measure of the effective
dimensionality of a model. We show that the learning capacity is a tiny
fraction of the number of parameters for many deep networks trained on typical
datasets, depends upon the number of samples used for training, and is
numerically consistent with notions of capacity obtained from the PAC-Bayesian
framework. The test error as a function of the learning capacity does not
exhibit double descent. We show that the learning capacity of a model saturates
at very small and very large sample sizes; this provides guidelines, as to
whether one should procure more data or whether one should search for new
architectures, to improve performance. We show how the learning capacity can be
used to understand the effective dimensionality, even for non-parametric models
such as random forests and $k$-nearest neighbor classifiers.
|
[
"cs.LG",
"cs.IT",
"math.IT"
] | false |
2305.17352
|
2023-05-27T03:15:24Z
|
Is Centralized Training with Decentralized Execution Framework
Centralized Enough for MARL?
|
[
"Yihe Zhou",
"Shunyu Liu",
"Yunpeng Qing",
"Kaixuan Chen",
"Tongya Zheng",
"Yanhao Huang",
"Jie Song",
"Mingli Song"
] |
Centralized Training with Decentralized Execution (CTDE) has recently emerged
as a popular framework for cooperative Multi-Agent Reinforcement Learning
(MARL), where agents can use additional global state information to guide
training in a centralized way and make their own decisions only based on
decentralized local policies. Despite the encouraging results achieved, CTDE
makes an independence assumption on agent policies, which limits agents to
adopt global cooperative information from each other during centralized
training. Therefore, we argue that existing CTDE methods cannot fully utilize
global information for training, leading to an inefficient joint-policy
exploration and even suboptimal results. In this paper, we introduce a novel
Centralized Advising and Decentralized Pruning (CADP) framework for multi-agent
reinforcement learning, that not only enables an efficacious message exchange
among agents during training but also guarantees the independent policies for
execution. Firstly, CADP endows agents the explicit communication channel to
seek and take advices from different agents for more centralized training. To
further ensure the decentralized execution, we propose a smooth model pruning
mechanism to progressively constraint the agent communication into a closed one
without degradation in agent cooperation capability. Empirical evaluations on
StarCraft II micromanagement and Google Research Football benchmarks
demonstrate that the proposed framework achieves superior performance compared
with the state-of-the-art counterparts. Our code will be made publicly
available.
|
[
"cs.AI",
"cs.LG",
"cs.MA"
] | false |
2305.17387
|
2023-05-27T06:46:08Z
|
Learning from Integral Losses in Physics Informed Neural Networks
|
[
"Ehsan Saleh",
"Saba Ghaffari",
"Timothy Bretl",
"Luke Olson",
"Matthew West"
] |
This work proposes a solution for the problem of training physics informed
networks under partial integro-differential equations. These equations require
infinite or a large number of neural evaluations to construct a single residual
for training. As a result, accurate evaluation may be impractical, and we show
that naive approximations at replacing these integrals with unbiased estimates
lead to biased loss functions and solutions. To overcome this bias, we
investigate three types of solutions: the deterministic sampling approach, the
double-sampling trick, and the delayed target method. We consider three classes
of PDEs for benchmarking; one defining a Poisson problem with singular charges
and weak solutions, another involving weak solutions on electro-magnetic fields
and a Maxwell equation, and a third one defining a Smoluchowski coagulation
problem. Our numerical results confirm the existence of the aforementioned bias
in practice, and also show that our proposed delayed target approach can lead
to accurate solutions with comparable quality to ones estimated with a large
number of samples. Our implementation is open-source and available at
https://github.com/ehsansaleh/btspinn.
|
[
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] | false |
2305.17417
|
2023-05-27T08:53:26Z
|
Modeling Dynamic Heterogeneous Graph and Node Importance for Future
Citation Prediction
|
[
"Hao Geng",
"Deqing Wang",
"Fuzhen Zhuang",
"Xuehua Ming",
"Chenguang Du",
"Ting Jiang",
"Haolong Guo",
"Rui Liu"
] |
Accurate citation count prediction of newly published papers could help
editors and readers rapidly figure out the influential papers in the future.
Though many approaches are proposed to predict a paper's future citation, most
ignore the dynamic heterogeneous graph structure or node importance in academic
networks. To cope with this problem, we propose a Dynamic heterogeneous Graph
and Node Importance network (DGNI) learning framework, which fully leverages
the dynamic heterogeneous graph and node importance information to predict
future citation trends of newly published papers. First, a dynamic
heterogeneous network embedding module is provided to capture the dynamic
evolutionary trends of the whole academic network. Then, a node importance
embedding module is proposed to capture the global consistency relationship to
figure out each paper's node importance. Finally, the dynamic evolutionary
trend embeddings and node importance embeddings calculated above are combined
to jointly predict the future citation counts of each paper, by a log-normal
distribution model according to multi-faced paper node representations.
Extensive experiments on two large-scale datasets demonstrate that our model
significantly improves all indicators compared to the SOTA models.
|
[
"cs.DL",
"cs.LG",
"physics.soc-ph"
] | false |
2305.17557
|
2023-05-27T19:16:55Z
|
Fair Clustering via Hierarchical Fair-Dirichlet Process
|
[
"Abhisek Chakraborty",
"Anirban Bhattacharya",
"Debdeep Pati"
] |
The advent of ML-driven decision-making and policy formation has led to an
increasing focus on algorithmic fairness. As clustering is one of the most
commonly used unsupervised machine learning approaches, there has naturally
been a proliferation of literature on {\em fair clustering}. A popular notion
of fairness in clustering mandates the clusters to be {\em balanced}, i.e.,
each level of a protected attribute must be approximately equally represented
in each cluster. Building upon the original framework, this literature has
rapidly expanded in various aspects. In this article, we offer a novel
model-based formulation of fair clustering, complementing the existing
literature which is almost exclusively based on optimizing appropriate
objective functions.
|
[
"stat.ML",
"cs.CY",
"cs.LG"
] | false |
2305.19379
|
2023-05-27T07:43:19Z
|
Inter Subject Emotion Recognition Using Spatio-Temporal Features From
EEG Signal
|
[
"Mohammad Asif",
"Diya Srivastava",
"Aditya Gupta",
"Uma Shanker Tiwary"
] |
Inter-subject or subject-independent emotion recognition has been a
challenging task in affective computing. This work is about an
easy-to-implement emotion recognition model that classifies emotions from EEG
signals subject independently. It is based on the famous EEGNet architecture,
which is used in EEG-related BCIs. We used the Dataset on Emotion using
Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events
-- the precise information of the emotion timings that participants felt. The
model is a combination of regular, depthwise and separable convolution layers
of CNN to classify the emotions. The model has the capacity to learn the
spatial features of the EEG channels and the temporal features of the EEG
signals variability with time. The model is evaluated for the valence space
ratings. The model achieved an accuracy of 73.04%.
|
[
"cs.HC",
"cs.LG",
"eess.SP"
] | false |
2305.17531
|
2023-05-27T17:22:32Z
|
Probing reaction channels via reinforcement learning
|
[
"Senwei Liang",
"Aditya N. Singh",
"Yuanran Zhu",
"David T. Limmer",
"Chao Yang"
] |
We propose a reinforcement learning based method to identify important
configurations that connect reactant and product states along chemical reaction
paths. By shooting multiple trajectories from these configurations, we can
generate an ensemble of configurations that concentrate on the transition path
ensemble. This configuration ensemble can be effectively employed in a neural
network-based partial differential equation solver to obtain an approximation
solution of a restricted Backward Kolmogorov equation, even when the dimension
of the problem is very high. The resulting solution, known as the committor
function, encodes mechanistic information for the reaction and can in turn be
used to evaluate reaction rates.
|
[
"physics.chem-ph",
"cs.AI",
"cs.LG",
"cs.NA",
"math.NA"
] | false |
2305.17611
|
2023-05-28T02:38:53Z
|
Bayesian Decision Making to Localize Visual Queries in 2D
|
[
"Syed Asjad",
"Aniket Gupta",
"Hanumant Singh"
] |
This report describes our approach for the EGO4D 2023 Visual Query 2D
Localization Challenge. Our method aims to reduce the number of False Positives
(FP) that occur because of high similarity between the visual crop and the
proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our
method uses a transformer to determine similarity in higher dimensions which is
used as our prior belief. The results are then combined together with the
similarity in lower dimensions from the Siamese Head, acting as our
measurement, to generate a posterior which is then used to determine the final
similarity of the visual crop with the proposed bounding box. Our code is
publicly available $\href{https://github.com/s-m-asjad/EGO4D_VQ2D}{here}$.
|
[
"cs.CV"
] | false |
2305.17654
|
2023-05-28T07:41:10Z
|
MixDehazeNet : Mix Structure Block For Image Dehazing Network
|
[
"LiPing Lu",
"Qian Xiong",
"DuanFeng Chu",
"BingRong Xu"
] |
Image dehazing is a typical task in the low-level vision field. Previous
studies verified the effectiveness of the large convolutional kernel and
attention mechanism in dehazing. However, there are two drawbacks: the
multi-scale properties of an image are readily ignored when a large
convolutional kernel is introduced, and the standard series connection of an
attention module does not sufficiently consider an uneven hazy distribution. In
this paper, we propose a novel framework named Mix Structure Image Dehazing
Network (MixDehazeNet), which solves two issues mentioned above. Specifically,
it mainly consists of two parts: the multi-scale parallel large convolution
kernel module and the enhanced parallel attention module. Compared with a
single large kernel, parallel large kernels with multi-scale are more capable
of taking partial texture into account during the dehazing phase. In addition,
an enhanced parallel attention module is developed, in which parallel
connections of attention perform better at dehazing uneven hazy distribution.
Extensive experiments on three benchmarks demonstrate the effectiveness of our
proposed methods. For example, compared with the previous state-of-the-art
methods, MixDehazeNet achieves a significant improvement (42.62dB PSNR) on the
SOTS indoor dataset. The code is released in
https://github.com/AmeryXiong/MixDehazeNet.
|
[
"cs.CV"
] | false |
2305.17695
|
2023-05-28T11:39:51Z
|
k-NNN: Nearest Neighbors of Neighbors for Anomaly Detection
|
[
"Ori Nizan",
"Ayellet Tal"
] |
Anomaly detection aims at identifying images that deviate significantly from
the norm. We focus on algorithms that embed the normal training examples in
space and when given a test image, detect anomalies based on the features
distance to the k-nearest training neighbors. We propose a new operator that
takes into account the varying structure & importance of the features in the
embedding space. Interestingly, this is done by taking into account not only
the nearest neighbors, but also the neighbors of these neighbors (k-NNN). We
show that by simply replacing the nearest neighbor component in existing
algorithms by our k-NNN operator, while leaving the rest of the algorithms
untouched, each algorithms own results are improved. This is the case both for
common homogeneous datasets, such as flowers or nuts of a specific type, as
well as for more diverse datasets
|
[
"cs.CV"
] | false |
2305.17710
|
2023-05-28T12:31:27Z
|
OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth
Estimation
|
[
"Wentao Chao",
"Fuqing Duan",
"Xuechun Wang",
"Yingqian Wang",
"Guanghui Wang"
] |
Light field (LF) depth estimation is a crucial task with numerous practical
applications. However, mainstream methods based on the multi-view stereo (MVS)
are resource-intensive and time-consuming as they need to construct a finer
cost volume. To address this issue and achieve a better trade-off between
accuracy and efficiency, we propose an occlusion-aware cascade cost volume for
LF depth (disparity) estimation. Our cascaded strategy reduces the sampling
number while keeping the sampling interval constant during the construction of
a finer cost volume. We also introduce occlusion maps to enhance accuracy in
constructing the occlusion-aware cost volume. Specifically, we first obtain the
coarse disparity map through the coarse disparity estimation network. Then, the
sub-aperture images (SAIs) of side views are warped to the center view based on
the initial disparity map. Next, we propose photo-consistency constraints
between the warped SAIs and the center SAI to generate occlusion maps for each
SAI. Finally, we introduce the coarse disparity map and occlusion maps to
construct an occlusion-aware refined cost volume, enabling the refined
disparity estimation network to yield a more precise disparity map. Extensive
experiments demonstrate the effectiveness of our method. Compared with
state-of-the-art methods, our method achieves a superior balance between
accuracy and efficiency and ranks first in terms of MSE and Q25 metrics among
published methods on the HCI 4D benchmark. The code and model of the proposed
method are available at https://github.com/chaowentao/OccCasNet.
|
[
"cs.CV"
] | false |
2305.17763
|
2023-05-28T16:18:41Z
|
NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization
|
[
"Zhixiang Min",
"Bingbing Zhuang",
"Samuel Schulter",
"Buyu Liu",
"Enrique Dunn",
"Manmohan Chandraker"
] |
Monocular 3D object localization in driving scenes is a crucial task, but
challenging due to its ill-posed nature. Estimating 3D coordinates for each
pixel on the object surface holds great potential as it provides dense 2D-3D
geometric constraints for the underlying PnP problem. However, high-quality
ground truth supervision is not available in driving scenes due to sparsity and
various artifacts of Lidar data, as well as the practical infeasibility of
collecting per-instance CAD models. In this work, we present NeurOCS, a
framework that uses instance masks and 3D boxes as input to learn 3D object
shapes by means of differentiable rendering, which further serves as
supervision for learning dense object coordinates. Our approach rests on
insights in learning a category-level shape prior directly from real driving
scenes, while properly handling single-view ambiguities. Furthermore, we study
and make critical design choices to learn object coordinates more effectively
from an object-centric view. Altogether, our framework leads to new
state-of-the-art in monocular 3D localization that ranks 1st on the
KITTI-Object benchmark among published monocular methods.
|
[
"cs.CV"
] | false |
2305.17768
|
2023-05-28T16:28:49Z
|
AIMS: All-Inclusive Multi-Level Segmentation
|
[
"Lu Qi",
"Jason Kuen",
"Weidong Guo",
"Jiuxiang Gu",
"Zhe Lin",
"Bo Du",
"Yu Xu",
"Ming-Hsuan Yang"
] |
Despite the progress of image segmentation for accurate visual entity
segmentation, completing the diverse requirements of image editing applications
for different-level region-of-interest selections remains unsolved. In this
paper, we propose a new task, All-Inclusive Multi-Level Segmentation (AIMS),
which segments visual regions into three levels: part, entity, and relation
(two entities with some semantic relationships). We also build a unified AIMS
model through multi-dataset multi-task training to address the two major
challenges of annotation inconsistency and task correlation. Specifically, we
propose task complementarity, association, and prompt mask encoder for
three-level predictions. Extensive experiments demonstrate the effectiveness
and generalization capacity of our method compared to other state-of-the-art
methods on a single dataset or the concurrent work on segmenting anything. We
will make our code and training model publicly available.
|
[
"cs.CV"
] | false |
2305.17785
|
2023-05-28T18:06:46Z
|
Lighting and Rotation Invariant Real-time Vehicle Wheel Detector based
on YOLOv5
|
[
"Michael Shenoda"
] |
Creating an object detector, in computer vision, has some common challenges
when initially developed based on Convolutional Neural Network (CNN)
architecture. These challenges are more apparent when creating model that needs
to adapt to images captured by various camera orientations, lighting
conditions, and environmental changes. The availability of the initial training
samples to cover all these conditions can be an enormous challenge with a time
and cost burden. While the problem can exist when creating any type of object
detection, some types are less common and have no pre-labeled image datasets
that exists publicly. Sometime public datasets are not reliable nor
comprehensive for a rare object type. Vehicle wheel is one of those example
that been chosen to demonstrate the approach of creating a lighting and
rotation invariant real-time detector based on YOLOv5 architecture. The
objective is to provide a simple approach that could be used as a reference for
developing other types of real-time object detectors.
|
[
"cs.CV"
] | false |
2305.17786
|
2023-05-28T18:17:31Z
|
Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch
|
[
"Michael Shenoda"
] |
Real-time object detection is a crucial problem to solve when in comes to
computer vision systems that needs to make appropriate decision based on
detection in a timely manner. I have chosen the YOLO v1 architecture to
implement it using PyTorch framework, with goal to familiarize with entire
object detection pipeline I attempted different techniques to modify the
original architecture to improve the results. Finally, I compare the metrics of
my implementation to the original.
|
[
"cs.CV"
] | false |
2305.17791
|
2023-05-28T18:34:59Z
|
LowDINO -- A Low Parameter Self Supervised Learning Model
|
[
"Sai Krishna Prathapaneni",
"Shvejan Shashank",
"Srikar Reddy K"
] |
This research aims to explore the possibility of designing a neural network
architecture that allows for small networks to adopt the properties of huge
networks, which have shown success in self-supervised learning (SSL), for all
the downstream tasks like image classification, segmentation, etc. Previous
studies have shown that using convolutional neural networks (ConvNets) can
provide inherent inductive bias, which is crucial for learning representations
in deep learning models. To reduce the number of parameters, attention
mechanisms are utilized through the usage of MobileViT blocks, resulting in a
model with less than 5 million parameters. The model is trained using
self-distillation with momentum encoder and a student-teacher architecture is
also employed, where the teacher weights use vision transformers (ViTs) from
recent SOTA SSL models. The model is trained on the ImageNet1k dataset. This
research provides an approach for designing smaller, more efficient neural
network architectures that can perform SSL tasks comparable to heavy models
|
[
"cs.CV"
] | false |
2305.17820
|
2023-05-28T22:47:54Z
|
Analysis of ROC for Edge Detectors
|
[
"Kai Yi Ji"
] |
This paper presents an evaluation of edge detectors using receiver operating
characteristic (ROC) analysis on the BIPED dataset. Our study examines the
benefits and drawbacks of applying this technique in Matlab. We observed that
while ROC analysis is suitable for certain edge filters, but for filters such
as Laplacian, Laplacian of Gaussian, and Canny, it presents challenges when
accurately measuring their performance using ROC metrics. To address this
issue, we introduce customization techniques to enhance the performance of
these filters, enabling more accurate evaluation. Through our customization
efforts, we achieved improved results, ultimately facilitating a comprehensive
assessment of the edge detectors.
|
[
"cs.CV"
] | false |
2308.05179
|
2023-05-28T15:51:35Z
|
JutePestDetect: An Intelligent Approach for Jute Pest Identification
Using Fine-Tuned Transfer Learning
|
[
"Md. Simul Hasan Talukder",
"Mohammad Raziuddin Chowdhury",
"Md Sakib Ullah Sourav",
"Abdullah Al Rakin",
"Shabbir Ahmed Shuvo",
"Rejwan Bin Sulaiman",
"Musarrat Saberin Nipun",
"Muntarin Islam",
"Mst Rumpa Islam",
"Md Aminul Islam",
"Zubaer Haque"
] |
In certain Asian countries, Jute is one of the primary sources of income and
Gross Domestic Product (GDP) for the agricultural sector. Like many other
crops, Jute is prone to pest infestations, and its identification is typically
made visually in countries like Bangladesh, India, Myanmar, and China. In
addition, this method is time-consuming, challenging, and somewhat imprecise,
which poses a substantial financial risk. To address this issue, the study
proposes a high-performing and resilient transfer learning (TL) based
JutePestDetect model to identify jute pests at the early stage. Firstly, we
prepared jute pest dataset containing 17 classes and around 380 photos per pest
class, which were evaluated after manual and automatic pre-processing and
cleaning, such as background removal and resizing. Subsequently, five prominent
pre-trained models -DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50
were selected from a previous study to design the JutePestDetect model. Each
model was revised by replacing the classification layer with a global average
pooling layer and incorporating a dropout layer for regularization. To evaluate
the models performance, various metrics such as precision, recall, F1 score,
ROC curve, and confusion matrix were employed. These analyses provided
additional insights for determining the efficacy of the models. Among them, the
customized regularized DenseNet201-based proposed JutePestDetect model
outperformed the others, achieving an impressive accuracy of 99%. As a result,
our proposed method and strategy offer an enhanced approach to pest
identification in the case of Jute, which can significantly benefit farmers
worldwide.
|
[
"cs.CV"
] | false |
2305.17624
|
2023-05-28T04:05:24Z
|
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object
Segmentation Network
|
[
"Chuong Huynh",
"Yuqian Zhou",
"Zhe Lin",
"Connelly Barnes",
"Eli Shechtman",
"Sohrab Amirghodsi",
"Abhinav Shrivastava"
] |
In photo editing, it is common practice to remove visual distractions to
improve the overall image quality and highlight the primary subject. However,
manually selecting and removing these small and dense distracting regions can
be a laborious and time-consuming task. In this paper, we propose an
interactive distractor selection method that is optimized to achieve the task
with just a single click. Our method surpasses the precision and recall
achieved by the traditional method of running panoptic segmentation and then
selecting the segments containing the clicks. We also showcase how a
transformer-based module can be used to identify more distracting regions
similar to the user's click position. Our experiments demonstrate that the
model can effectively and accurately segment unknown distracting objects
interactively and in groups. By significantly simplifying the photo cleaning
and retouching process, our proposed model provides inspiration for exploring
rare object segmentation and group selection with a single click.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17652
|
2023-05-28T07:16:44Z
|
ConaCLIP: Exploring Distillation of Fully-Connected Knowledge
Interaction Graph for Lightweight Text-Image Retrieval
|
[
"Jiapeng Wang",
"Chengyu Wang",
"Xiaodan Wang",
"Jun Huang",
"Lianwen Jin"
] |
Large-scale pre-trained text-image models with dual-encoder architectures
(such as CLIP) are typically adopted for various vision-language applications,
including text-image retrieval. However,these models are still less practical
on edge devices or for real-time situations, due to the substantial indexing
and inference time and the large consumption of computational resources.
Although knowledge distillation techniques have been widely utilized for
uni-modal model compression, how to expand them to the situation when the
numbers of modalities and teachers/students are doubled has been rarely
studied. In this paper, we conduct comprehensive experiments on this topic and
propose the fully-Connected knowledge interaction graph (Cona) technique for
cross-modal pre-training distillation. Based on our findings, the resulting
ConaCLIP achieves SOTA performances on the widely-used Flickr30K and MSCOCO
benchmarks under the lightweight setting. An industry application of our method
on an e-commercial platform further demonstrates the significant effectiveness
of ConaCLIP.
|
[
"cs.CV",
"cs.CL"
] | false |
2305.17714
|
2023-05-28T12:57:20Z
|
An Open-Source Gloss-Based Baseline for Spoken to Signed Language
Translation
|
[
"Amit Moryossef",
"Mathias Müller",
"Anne Göhring",
"Zifan Jiang",
"Yoav Goldberg",
"Sarah Ebling"
] |
Sign language translation systems are complex and require many components. As
a result, it is very hard to compare methods across publications. We present an
open-source implementation of a text-to-gloss-to-pose-to-video pipeline
approach, demonstrating conversion from German to Swiss German Sign Language,
French to French Sign Language of Switzerland, and Italian to Italian Sign
Language of Switzerland. We propose three different components for the
text-to-gloss translation: a lemmatizer, a rule-based word reordering and
dropping component, and a neural machine translation system. Gloss-to-pose
conversion occurs using data from a lexicon for three different signed
languages, with skeletal poses extracted from videos. To generate a sentence,
the text-to-gloss system is first run, and the pose representations of the
resulting signs are stitched together.
|
[
"cs.CL",
"cs.CV"
] | false |
2305.17748
|
2023-05-28T15:04:26Z
|
Image Hash Minimization for Tamper Detection
|
[
"Subhajit Maity",
"Ram Kumar Karsh"
] |
Tamper detection using image hash is a very common problem of modern days.
Several research and advancements have already been done to address this
problem. However, most of the existing methods lack the accuracy of tamper
detection when the tampered area is low, as well as requiring long image
hashes. In this paper, we propose a novel method objectively to minimize the
hash length while enhancing the performance at low tampered area.
|
[
"cs.CV",
"eess.IV"
] | false |
2305.17784
|
2023-05-28T17:59:26Z
|
ConvGenVisMo: Evaluation of Conversational Generative Vision Models
|
[
"Narjes Nikzad Khasmakhi",
"Meysam Asgari-Chenaghlu",
"Nabiha Asghar",
"Philipp Schaer",
"Dietlind Zühlke"
] |
Conversational generative vision models (CGVMs) like Visual ChatGPT (Wu et
al., 2023) have recently emerged from the synthesis of computer vision and
natural language processing techniques. These models enable more natural and
interactive communication between humans and machines, because they can
understand verbal inputs from users and generate responses in natural language
along with visual outputs. To make informed decisions about the usage and
deployment of these models, it is important to analyze their performance
through a suitable evaluation framework on realistic datasets. In this paper,
we present ConvGenVisMo, a framework for the novel task of evaluating CGVMs.
ConvGenVisMo introduces a new benchmark evaluation dataset for this task, and
also provides a suite of existing and new automated evaluation metrics to
evaluate the outputs. All ConvGenVisMo assets, including the dataset and the
evaluation code, will be made available publicly on GitHub.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17828
|
2023-05-28T23:42:35Z
|
Counter-Hypothetical Particle Filters for Single Object Pose Tracking
|
[
"Elizabeth A. Olson",
"Jana Pavlasek",
"Jasmine A. Berry",
"Odest Chadwicke Jenkins"
] |
Particle filtering is a common technique for six degree of freedom (6D) pose
estimation due to its ability to tractably represent belief over object pose.
However, the particle filter is prone to particle deprivation due to the
high-dimensional nature of 6D pose. When particle deprivation occurs, it can
cause mode collapse of the underlying belief distribution during importance
sampling. If the region surrounding the true state suffers from mode collapse,
recovering its belief is challenging since the area is no longer represented in
the probability mass formed by the particles. Previous methods mitigate this
problem by randomizing and resetting particles in the belief distribution, but
determining the frequency of reinvigoration has relied on hand-tuning abstract
heuristics. In this paper, we estimate the necessary reinvigoration rate at
each time step by introducing a Counter-Hypothetical likelihood function, which
is used alongside the standard likelihood. Inspired by the notions of
plausibility and implausibility from Evidential Reasoning, the addition of our
Counter-Hypothetical likelihood function assigns a level of doubt to each
particle. The competing cumulative values of confidence and doubt across the
particle set are used to estimate the level of failure within the filter, in
order to determine the portion of particles to be reinvigorated. We demonstrate
the effectiveness of our method on the rigid body object 6D pose tracking task.
|
[
"cs.RO",
"cs.CV"
] | false |
2305.18373
|
2023-05-28T04:49:01Z
|
KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature
Adaptation of Vision-Language Models
|
[
"Zhiwei Jia",
"Pradyumna Narayana",
"Arjun R. Akula",
"Garima Pruthi",
"Hao Su",
"Sugato Basu",
"Varun Jampani"
] |
Image ad understanding is a crucial task with wide real-world applications.
Although highly challenging with the involvement of diverse atypical scenes,
real-world entities, and reasoning over scene-texts, how to interpret image ads
is relatively under-explored, especially in the era of foundational
vision-language models (VLMs) featuring impressive generalizability and
adaptability. In this paper, we perform the first empirical study of image ad
understanding through the lens of pre-trained VLMs. We benchmark and reveal
practical challenges in adapting these VLMs to image ad understanding. We
propose a simple feature adaptation strategy to effectively fuse multimodal
information for image ads and further empower it with knowledge of real-world
entities. We hope our study draws more attention to image ad understanding
which is broadly relevant to the advertising industry.
|
[
"cs.CV",
"cs.CL"
] | true |
2305.18424
|
2023-05-28T20:38:13Z
|
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
|
[
"Patrik Okanovic",
"Roger Waleffe",
"Vasilis Mageirakos",
"Konstantinos E. Nikolakakis",
"Amin Karbasi",
"Dionysis Kalogerias",
"Nezihe Merve Gürel",
"Theodoros Rekatsinas"
] |
Methods for carefully selecting or generating a small set of training data to
learn from, i.e., data pruning, coreset selection, and data distillation, have
been shown to be effective in reducing the ever-increasing cost of training
neural networks. Behind this success are rigorously designed strategies for
identifying informative training examples out of large datasets. However, these
strategies come with additional computational costs associated with subset
selection or data distillation before training begins, and furthermore, many
are shown to even under-perform random sampling in high data compression
regimes. As such, many data pruning, coreset selection, or distillation methods
may not reduce 'time-to-accuracy', which has become a critical efficiency
measure of training deep neural networks over large datasets. In this work, we
revisit a powerful yet overlooked random sampling strategy to address these
challenges and introduce an approach called Repeated Sampling of Random Subsets
(RSRS or RS2), where we randomly sample the subset of training data for each
epoch of model training. We test RS2 against thirty state-of-the-art data
pruning and data distillation methods across four datasets including ImageNet.
Our results demonstrate that RS2 significantly reduces time-to-accuracy
compared to existing techniques. For example, when training on ImageNet in the
high-compression regime (using less than 10% of the dataset each epoch), RS2
yields accuracy improvements up to 29% compared to competing pruning methods
while offering a runtime reduction of 7x. Beyond the above meta-study, we
provide a convergence analysis for RS2 and discuss its generalization
capability. The primary goal of our work is to establish RS2 as a competitive
baseline for future data selection or distillation techniques aimed at
efficient training.
|
[
"cs.LG",
"cs.CV"
] | false |
2305.18433
|
2023-05-28T23:54:52Z
|
Cognitively Inspired Cross-Modal Data Generation Using Diffusion Models
|
[
"Zizhao Hu",
"Mohammad Rostami"
] |
Most existing cross-modal generative methods based on diffusion models use
guidance to provide control over the latent space to enable conditional
generation across different modalities. Such methods focus on providing
guidance through separately-trained models, each for one modality. As a result,
these methods suffer from cross-modal information loss and are limited to
unidirectional conditional generation. Inspired by how humans synchronously
acquire multi-modal information and learn the correlation between modalities,
we explore a multi-modal diffusion model training and sampling scheme that uses
channel-wise image conditioning to learn cross-modality correlation during the
training phase to better mimic the learning process in the brain. Our empirical
results demonstrate that our approach can achieve data generation conditioned
on all correlated modalities.
|
[
"cs.LG",
"cs.CV"
] | false |
2305.19146
|
2023-05-28T16:52:25Z
|
ASU-CNN: An Efficient Deep Architecture for Image Classification and
Feature Visualizations
|
[
"Jamshaid Ul Rahman",
"Faiza Makhdoom",
"Dianchen Lu"
] |
Activation functions play a decisive role in determining the capacity of Deep
Neural Networks as they enable neural networks to capture inherent
nonlinearities present in data fed to them. The prior research on activation
functions primarily focused on the utility of monotonic or non-oscillatory
functions, until Growing Cosine Unit broke the taboo for a number of
applications. In this paper, a Convolutional Neural Network model named as
ASU-CNN is proposed which utilizes recently designed activation function ASU
across its layers. The effect of this non-monotonic and oscillatory function is
inspected through feature map visualizations from different convolutional
layers. The optimization of proposed network is offered by Adam with a
fine-tuned adjustment of learning rate. The network achieved promising results
on both training and testing data for the classification of CIFAR-10. The
experimental results affirm the computational feasibility and efficacy of the
proposed model for performing tasks related to the field of computer vision.
|
[
"cs.CV",
"cs.LG"
] | false |
2306.00835
|
2023-05-28T10:46:18Z
|
Reconstructing Sea Surface Temperature Images: A Masked Autoencoder
Approach for Cloud Masking and Reconstruction
|
[
"Angelina Agabin",
"J. Xavier Prochaska"
] |
This thesis presents a new algorithm to mitigate cloud masking in the
analysis of sea surface temperature (SST) data generated by remote sensing
technologies, e.g., Clouds interfere with the analysis of all remote sensing
data using wavelengths shorter than 12 microns, significantly limiting the
quantity of usable data and creating a biased geographical distribution
(towards equatorial and coastal regions). To address this issue, we propose an
unsupervised machine learning algorithm called Enki which uses a Vision
Transformer with Masked Autoencoding to reconstruct masked pixels. We train
four different models of Enki with varying mask ratios (t) of 10%, 35%, 50%,
and 75% on the generated Ocean General Circulation Model (OGCM) dataset
referred to as LLC4320. To evaluate performance, we reconstruct a validation
set of LLC4320 SST images with random ``clouds'' corrupting p=10%, 20%, 30%,
40%, 50% of the images with individual patches of 4x4 pixel^2. We consistently
find that at all levels of p there is one or multiple models that reconstruct
the images with a mean RMSE of less than 0.03K, i.e. lower than the estimated
sensor error of VIIRS data. Similarly, at the individual patch level, the
reconstructions have RMSE 8x smaller than the fluctuations in the patch. And,
as anticipated, reconstruction errors are larger for images with a higher
degree of complexity. Our analysis also reveals that patches along the image
border have systematically higher reconstruction error; we recommend ignoring
these in production. We conclude that Enki shows great promise to surpass
in-painting as a means of reconstructing cloud masking. Future research will
develop Enki to reconstruct real-world data.
|
[
"cs.CV",
"physics.ao-ph"
] | false |
2305.18387
|
2023-05-28T10:52:03Z
|
Augmenting Character Designers Creativity Using Generative Adversarial
Networks
|
[
"Mohammad Lataifeh",
"Xavier Carrasco",
"Ashraf Elnagar",
"Naveed Ahmed"
] |
Recent advances in Generative Adversarial Networks (GANs) continue to attract
the attention of researchers in different fields due to the wide range of
applications devised to take advantage of their key features. Most recent GANs
are focused on realism, however, generating hyper-realistic output is not a
priority for some domains, as in the case of this work. The generated outcomes
are used here as cognitive components to augment character designers creativity
while conceptualizing new characters for different multimedia projects. To
select the best-suited GANs for such a creative context, we first present a
comparison between different GAN architectures and their performance when
trained from scratch on a new visual characters dataset using a single Graphics
Processing Unit. We also explore alternative techniques, such as transfer
learning and data augmentation, to overcome computational resource limitations,
a challenge faced by many researchers in the domain. Additionally, mixed
methods are used to evaluate the cognitive value of the generated visuals on
character designers agency conceptualizing new characters. The results
discussed proved highly effective for this context, as demonstrated by early
adaptations to the characters design process. As an extension for this work,
the presented approach will be further evaluated as a novel co-design process
between humans and machines to investigate where and how the generated concepts
are interacting with and influencing the design process outcome.
|
[
"cs.HC",
"cs.CV",
"cs.LG"
] | false |
2305.18398
|
2023-05-28T13:35:50Z
|
Mitigating Inappropriateness in Image Generation: Can there be Value in
Reflecting the World's Ugliness?
|
[
"Manuel Brack",
"Felix Friedrich",
"Patrick Schramowski",
"Kristian Kersting"
] |
Text-conditioned image generation models have recently achieved astonishing
results in image quality and text alignment and are consequently employed in a
fast-growing number of applications. Since they are highly data-driven, relying
on billion-sized datasets randomly scraped from the web, they also reproduce
inappropriate human behavior. Specifically, we demonstrate inappropriate
degeneration on a large-scale for various generative text-to-image models, thus
motivating the need for monitoring and moderating them at deployment. To this
end, we evaluate mitigation strategies at inference to suppress the generation
of inappropriate content. Our findings show that we can use models'
representations of the world's ugliness to align them with human preferences.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.19129
|
2023-05-28T20:26:06Z
|
Key-Value Transformer
|
[
"Ali Borji"
] |
Transformers have emerged as the prevailing standard solution for various AI
tasks, including computer vision and natural language processing. The widely
adopted Query, Key, and Value formulation (QKV) has played a significant role
in this. Nevertheless, no research has examined the essentiality of these three
components for transformer performance. Therefore, we conducted an evaluation
of the key-value formulation (KV), which generates symmetric attention maps,
along with an asymmetric version that incorporates a 2D positional encoding
into the attention matrix. Remarkably, this transformer requires fewer
parameters and computation than the original one. Through experiments
encompassing three task types -- synthetics (such as reversing or sorting a
list), vision (mnist or cifar classification), and NLP (character generation
and translation) -- we discovered that the KV transformer occasionally
outperforms the QKV transformer. However, it also exhibits instances of
underperformance compared to QKV, making it challenging to draw a definitive
conclusion. Nonetheless, we consider the reported results to be encouraging and
anticipate that they may pave the way for more efficient transformers in the
future.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.17607
|
2023-05-28T02:09:08Z
|
More than Classification: A Unified Framework for Event Temporal
Relation Extraction
|
[
"Quzhe Huang",
"Yutong Hu",
"Shengqi Zhu",
"Yansong Feng",
"Chang Liu",
"Dongyan Zhao"
] |
Event temporal relation extraction~(ETRE) is usually formulated as a
multi-label classification task, where each type of relation is simply treated
as a one-hot label. This formulation ignores the meaning of relations and wipes
out their intrinsic dependency. After examining the relation definitions in
various ETRE tasks, we observe that all relations can be interpreted using the
start and end time points of events. For example, relation \textit{Includes}
could be interpreted as event 1 starting no later than event 2 and ending no
earlier than event 2. In this paper, we propose a unified event temporal
relation extraction framework, which transforms temporal relations into logical
expressions of time points and completes the ETRE by predicting the relations
between certain time point pairs. Experiments on TB-Dense and MATRES show
significant improvements over a strong baseline and outperform the
state-of-the-art model by 0.3\% on both datasets. By representing all relations
in a unified framework, we can leverage the relations with sufficient data to
assist the learning of other relations, thus achieving stable improvement in
low-data scenarios. When the relation definitions are changed, our method can
quickly adapt to the new ones by simply modifying the logic expressions that
map time points to new event relations. The code is released at
\url{https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE}.
|
[
"cs.CL"
] | false |
2305.17653
|
2023-05-28T07:27:12Z
|
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
|
[
"Zhicheng Guo",
"Sijie Cheng",
"Yile Wang",
"Peng Li",
"Yang Liu"
] |
Retrieval-augmented methods have received increasing attention to support
downstream tasks by leveraging useful information from external resources.
Recent studies mainly focus on exploring retrieval to solve knowledge-intensive
(KI) tasks. However, the potential of retrieval for most
non-knowledge-intensive (NKI) tasks remains under-explored. There are two main
challenges to leveraging retrieval-augmented methods for NKI tasks: 1) the
demand for diverse relevance score functions and 2) the dilemma between
training cost and task performance. To address these challenges, we propose a
two-stage framework for NKI tasks, named PGRA. In the first stage, we adopt a
task-agnostic retriever to build a shared static index and select candidate
evidence efficiently. In the second stage, we design a prompt-guided reranker
to rerank the nearest evidence according to task-specific relevance for the
reader. Experimental results show that PGRA outperforms other state-of-the-art
retrieval-augmented methods. Our analyses further investigate the influence
factors to model performance and demonstrate the generality of PGRA. Codes are
available at https://github.com/THUNLP-MT/PGRA.
|
[
"cs.CL"
] | false |
2305.17660
|
2023-05-28T08:01:40Z
|
Plug-and-Play Document Modules for Pre-trained Models
|
[
"Chaojun Xiao",
"Zhengyan Zhang",
"Xu Han",
"Chi-Min Chan",
"Yankai Lin",
"Zhiyuan Liu",
"Xiangyang Li",
"Zhonghua Li",
"Zhao Cao",
"Maosong Sun"
] |
Large-scale pre-trained models (PTMs) have been widely used in
document-oriented NLP tasks, such as question answering. However, the
encoding-task coupling requirement results in the repeated encoding of the same
documents for different tasks and queries, which is highly computationally
inefficient. To this end, we target to decouple document encoding from
downstream tasks, and propose to represent each document as a plug-and-play
document module, i.e., a document plugin, for PTMs (PlugD). By inserting
document plugins into the backbone PTM for downstream tasks, we can encode a
document one time to handle multiple tasks, which is more efficient than
conventional encoding-task coupling methods that simultaneously encode
documents and input queries using task-specific encoders. Extensive experiments
on 8 datasets of 4 typical NLP tasks show that PlugD enables models to encode
documents once and for all across different scenarios. Especially, PlugD can
save $69\%$ computational costs while achieving comparable performance to
state-of-the-art encoding-task coupling methods. Additionally, we show that
PlugD can serve as an effective post-processing way to inject knowledge into
task-specific models, improving model performance without any additional model
training.
|
[
"cs.CL"
] | false |
2305.17663
|
2023-05-28T08:17:07Z
|
Lexical Retrieval Hypothesis in Multimodal Context
|
[
"Po-Ya Angela Wang",
"Pin-Er Chen",
"Hsin-Yu Chou",
"Yu-Hsiang Tseng",
"Shu-Kai Hsieh"
] |
Multimodal corpora have become an essential language resource for language
science and grounded natural language processing (NLP) systems due to the
growing need to understand and interpret human communication across various
channels. In this paper, we first present our efforts in building the first
Multimodal Corpus for Languages in Taiwan (MultiMoco). Based on the corpus, we
conduct a case study investigating the Lexical Retrieval Hypothesis (LRH),
specifically examining whether the hand gestures co-occurring with speech
constants facilitate lexical retrieval or serve other discourse functions. With
detailed annotations on eight parliamentary interpellations in Taiwan Mandarin,
we explore the co-occurrence between speech constants and non-verbal features
(i.e., head movement, face movement, hand gesture, and function of hand
gesture). Our findings suggest that while hand gestures do serve as
facilitators for lexical retrieval in some cases, they also serve the purpose
of information emphasis. This study highlights the potential of the MultiMoco
Corpus to provide an important resource for in-depth analysis and further
research in multimodal communication studies.
|
[
"cs.CL"
] | false |
2305.17670
|
2023-05-28T09:22:44Z
|
Stochastic Bridges as Effective Regularizers for Parameter-Efficient
Tuning
|
[
"Weize Chen",
"Xu Han",
"Yankai Lin",
"Zhiyuan Liu",
"Maosong Sun",
"Jie Zhou"
] |
Parameter-efficient tuning methods (PETs) have achieved promising results in
tuning large pre-trained language models (PLMs). By formalizing frozen PLMs and
additional tunable parameters as systems and controls respectively, PETs can be
theoretically grounded to optimal control and further viewed as optimizing the
terminal cost and running cost in the optimal control literature. Despite the
elegance of this theoretical grounding, in practice, existing PETs often ignore
the running cost and only optimize the terminal cost, i.e., focus on optimizing
the loss function of the output state, regardless of the running cost that
depends on the intermediate states. Since it is non-trivial to directly model
the intermediate states and design a running cost function, we propose to use
latent stochastic bridges to regularize the intermediate states and use the
regularization as the running cost of PETs. As the first work to propose
regularized PETs that use stochastic bridges as the regularizers (running
costs) for the intermediate states, we show the effectiveness and generality of
this regularization across different tasks, PLMs and PETs. In view of the great
potential and capacity, we believe more sophisticated regularizers can be
designed for PETs and better performance can be achieved in the future. The
code is released at
\url{https://github.com/thunlp/stochastic-bridge-pet/tree/main}.
|
[
"cs.CL"
] | false |
2305.17679
|
2023-05-28T10:04:15Z
|
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian
News Texts
|
[
"Anton Golubev",
"Nicolay Rusnachenko",
"Natalia Loukachevitch"
] |
The paper describes the RuSentNE-2023 evaluation devoted to targeted
sentiment analysis in Russian news texts. The task is to predict sentiment
towards a named entity in a single sentence. The dataset for RuSentNE-2023
evaluation is based on the Russian news corpus RuSentNE having rich
sentiment-related annotation. The corpus is annotated with named entities and
sentiments towards these entities, along with related effects and emotional
states. The evaluation was organized using the CodaLab competition framework.
The main evaluation measure was macro-averaged measure of positive and negative
classes. The best results achieved were of 66% Macro F-measure
(Positive+Negative classes). We also tested ChatGPT on the test set from our
evaluation and found that the zero-shot answers provided by ChatGPT reached 60%
of the F-measure, which corresponds to 4th place in the evaluation. ChatGPT
also provided detailed explanations of its conclusion. This can be considered
as quite high for zero-shot application.
|
[
"cs.CL",
"I.2.7"
] | false |
2305.17690
|
2023-05-28T10:55:31Z
|
HaVQA: A Dataset for Visual Question Answering and Multimodal Research
in Hausa Language
|
[
"Shantipriya Parida",
"Idris Abdulmumin",
"Shamsuddeen Hassan Muhammad",
"Aneesh Bose",
"Guneet Singh Kohli",
"Ibrahim Said Ahmad",
"Ketan Kotwal",
"Sayan Deb Sarkar",
"Ondřej Bojar",
"Habeebah Adamu Kakudi"
] |
This paper presents HaVQA, the first multimodal dataset for visual
question-answering (VQA) tasks in the Hausa language. The dataset was created
by manually translating 6,022 English question-answer pairs, which are
associated with 1,555 unique images from the Visual Genome dataset. As a
result, the dataset provides 12,044 gold standard English-Hausa parallel
sentences that were translated in a fashion that guarantees their semantic
match with the corresponding visual information. We conducted several baseline
experiments on the dataset, including visual question answering, visual
question elicitation, text-only and multimodal machine translation.
|
[
"cs.CL"
] | false |
2305.17696
|
2023-05-28T11:51:20Z
|
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable
Responses Created Through Human-Machine Collaboration
|
[
"Hwaran Lee",
"Seokhee Hong",
"Joonsuk Park",
"Takyoung Kim",
"Meeyoung Cha",
"Yejin Choi",
"Byoung Pil Kim",
"Gunhee Kim",
"Eun-Ju Lee",
"Yong Lim",
"Alice Oh",
"Sangchul Park",
"Jung-Woo Ha"
] |
The potential social harms that large language models pose, such as
generating offensive content and reinforcing biases, are steeply rising.
Existing works focus on coping with this concern while interacting with
ill-intentioned users, such as those who explicitly make hate speech or elicit
harmful responses. However, discussions on sensitive issues can become toxic
even if the users are well-intentioned. For safer models in such scenarios, we
present the Sensitive Questions and Acceptable Response (SQuARe) dataset, a
large-scale Korean dataset of 49k sensitive questions with 42k acceptable and
46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA
in a human-in-the-loop manner based on real news headlines. Experiments show
that acceptable response generation significantly improves for HyperCLOVA and
GPT-3, demonstrating the efficacy of this dataset.
|
[
"cs.CL"
] | false |
2305.17698
|
2023-05-28T11:58:07Z
|
Neural Machine Translation with Dynamic Graph Convolutional Decoder
|
[
"Lei Li",
"Kai Fan",
"Lingyu Yang",
"Hongjia Li",
"Chun Yuan"
] |
Existing wisdom demonstrates the significance of syntactic knowledge for the
improvement of neural machine translation models. However, most previous works
merely focus on leveraging the source syntax in the well-known encoder-decoder
framework. In sharp contrast, this paper proposes an end-to-end translation
architecture from the (graph \& sequence) structural inputs to the (graph \&
sequence) outputs, where the target translation and its corresponding syntactic
graph are jointly modeled and generated. We propose a customized Dynamic
Spatial-Temporal Graph Convolutional Decoder (Dyn-STGCD), which is designed for
consuming source feature representations and their syntactic graph, and
auto-regressively generating the target syntactic graph and tokens
simultaneously. We conduct extensive experiments on five widely acknowledged
translation benchmarks, verifying that our proposal achieves consistent
improvements over baselines and other syntax-aware variants.
|
[
"cs.CL"
] | false |
2305.17699
|
2023-05-28T12:01:34Z
|
Decoupling Pseudo Label Disambiguation and Representation Learning for
Generalized Intent Discovery
|
[
"Yutao Mou",
"Xiaoshuai Song",
"Keqing He",
"Chen Zeng",
"Pei Wang",
"Jingang Wang",
"Yunsen Xian",
"Weiran Xu"
] |
Generalized intent discovery aims to extend a closed-set in-domain intent
classifier to an open-world intent set including in-domain and out-of-domain
intents. The key challenges lie in pseudo label disambiguation and
representation learning. Previous methods suffer from a coupling of pseudo
label disambiguation and representation learning, that is, the reliability of
pseudo labels relies on representation learning, and representation learning is
restricted by pseudo labels in turn. In this paper, we propose a decoupled
prototype learning framework (DPL) to decouple pseudo label disambiguation and
representation learning. Specifically, we firstly introduce prototypical
contrastive representation learning (PCL) to get discriminative
representations. And then we adopt a prototype-based label disambiguation
method (PLD) to obtain pseudo labels. We theoretically prove that PCL and PLD
work in a collaborative fashion and facilitate pseudo label disambiguation.
Experiments and analysis on three benchmark datasets show the effectiveness of
our method.
|
[
"cs.CL"
] | false |
2305.17709
|
2023-05-28T12:30:23Z
|
Parallel Data Helps Neural Entity Coreference Resolution
|
[
"Gongbo Tang",
"Christian Hardmeier"
] |
Coreference resolution is the task of finding expressions that refer to the
same entity in a text. Coreference models are generally trained on monolingual
annotated data but annotating coreference is expensive and challenging.
Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric
knowledge, but it has not been explored in end-to-end neural models yet. In
this paper, we propose a simple yet effective model to exploit coreference
knowledge from parallel data. In addition to the conventional modules learning
coreference from annotations, we introduce an unsupervised module to capture
cross-lingual coreference knowledge. Our proposed cross-lingual model achieves
consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0
English dataset using 9 different synthetic parallel datasets. These
experimental results confirm that parallel data can provide additional
coreference knowledge which is beneficial to coreference resolution tasks.
|
[
"cs.CL"
] | false |
2305.17721
|
2023-05-28T13:19:12Z
|
Rethinking Masked Language Modeling for Chinese Spelling Correction
|
[
"Hongqiu Wu",
"Shaohua Zhang",
"Yuchen Zhang",
"Hai Zhao"
] |
In this paper, we study Chinese Spelling Correction (CSC) as a joint decision
made by two separate models: a language model and an error model. Through
empirical analysis, we find that fine-tuning BERT tends to over-fit the error
model while under-fit the language model, resulting in poor generalization to
out-of-distribution error patterns. Given that BERT is the backbone of most CSC
models, this phenomenon has a significant negative impact. To address this
issue, we are releasing a multi-domain benchmark LEMON, with higher quality and
diversity than existing benchmarks, to allow a comprehensive assessment of the
open domain generalization of CSC models. Then, we demonstrate that a very
simple strategy, randomly masking 20\% non-error tokens from the input sequence
during fine-tuning is sufficient for learning a much better language model
without sacrificing the error model. This technique can be applied to any model
architecture and achieves new state-of-the-art results on SIGHAN, ECSpell, and
LEMON.
|
[
"cs.CL"
] | false |
2305.17729
|
2023-05-28T13:59:58Z
|
Tri-level Joint Natural Language Understanding for Multi-turn
Conversational Datasets
|
[
"Henry Weld",
"Sijia Hu",
"Siqu Long",
"Josiah Poon",
"Soyeon Caren Han"
] |
Natural language understanding typically maps single utterances to a dual
level semantic frame, sentence level intent and slot labels at the word level.
The best performing models force explicit interaction between intent detection
and slot filling. We present a novel tri-level joint natural language
understanding approach, adding domain, and explicitly exchange semantic
information between all levels. This approach enables the use of multi-turn
datasets which are a more natural conversational environment than single
utterance. We evaluate our model on two multi-turn datasets for which we are
the first to conduct joint slot-filling and intent detection. Our model
outperforms state-of-the-art joint models in slot filling and intent detection
on multi-turn data sets. We provide an analysis of explicit interaction
locations between the layers. We conclude that including domain information
improves model performance.
|
[
"cs.CL"
] | false |
2305.17750
|
2023-05-28T15:14:54Z
|
Reliable and Interpretable Drift Detection in Streams of Short Texts
|
[
"Ella Rabinovich",
"Matan Vetzler",
"Samuel Ackerman",
"Ateret Anaby-Tavor"
] |
Data drift is the change in model input data that is one of the key factors
leading to machine learning models performance degradation over time.
Monitoring drift helps detecting these issues and preventing their harmful
consequences. Meaningful drift interpretation is a fundamental step towards
effective re-training of the model. In this study we propose an end-to-end
framework for reliable model-agnostic change-point detection and interpretation
in large task-oriented dialog systems, proven effective in multiple customer
deployments. We evaluate our approach and demonstrate its benefits with a novel
variant of intent classification training dataset, simulating customer requests
to a dialog system. We make the data publicly available.
|
[
"cs.CL"
] | false |
2305.17779
|
2023-05-28T17:22:04Z
|
Generating EDU Extracts for Plan-Guided Summary Re-Ranking
|
[
"Griffin Adams",
"Alexander R. Fabbri",
"Faisal Ladhak",
"Kathleen McKeown",
"Noémie Elhadad"
] |
Two-step approaches, in which summary candidates are generated-then-reranked
to return a single summary, can improve ROUGE scores over the standard
single-step approach. Yet, standard decoding methods (i.e., beam search,
nucleus sampling, and diverse beam search) produce candidates with redundant,
and often low quality, content. In this paper, we design a novel method to
generate candidates for re-ranking that addresses these issues. We ground each
candidate abstract on its own unique content plan and generate distinct
plan-guided abstracts using a model's top beam. More concretely, a standard
language model (a BART LM) auto-regressively generates elemental discourse unit
(EDU) content plans with an extractive copy mechanism. The top K beams from the
content plan generator are then used to guide a separate LM, which produces a
single abstractive candidate for each distinct plan. We apply an existing
re-ranker (BRIO) to abstractive candidates generated from our method, as well
as baseline decoding methods. We show large relevance improvements over
previously published methods on widely used single document news article
corpora, with ROUGE-2 F1 gains of 0.88, 2.01, and 0.38 on CNN / Dailymail, NYT,
and Xsum, respectively. A human evaluation on CNN / DM validates these results.
Similarly, on 1k samples from CNN / DM, we show that prompting GPT-3 to follow
EDU plans outperforms sampling-based methods by 1.05 ROUGE-2 F1 points. Code to
generate and realize plans is available at
https://github.com/griff4692/edu-sum.
|
[
"cs.CL"
] | false |
2305.17804
|
2023-05-28T19:36:50Z
|
Targeted Data Generation: Finding and Fixing Model Weaknesses
|
[
"Zexue He",
"Marco Tulio Ribeiro",
"Fereshte Khani"
] |
Even when aggregate accuracy is high, state-of-the-art NLP models often fail
systematically on specific subgroups of data, resulting in unfair outcomes and
eroding user trust. Additional data collection may not help in addressing these
weaknesses, as such challenging subgroups may be unknown to users, and
underrepresented in the existing and new data. We propose Targeted Data
Generation (TDG), a framework that automatically identifies challenging
subgroups, and generates new data for those subgroups using large language
models (LLMs) with a human in the loop. TDG estimates the expected benefit and
potential harm of data augmentation for each subgroup, and selects the ones
most likely to improve within group performance without hurting overall
performance. In our experiments, TDG significantly improves the accuracy on
challenging subgroups for state-of-the-art sentiment analysis and natural
language inference models, while also improving overall test accuracy.
|
[
"cs.CL"
] | false |
2305.17812
|
2023-05-28T20:49:52Z
|
Tab-CoT: Zero-shot Tabular Chain of Thought
|
[
"Ziqi Jin",
"Wei Lu"
] |
The chain-of-though (CoT) prompting methods were successful in various
natural language processing (NLP) tasks thanks to their ability to unveil the
underlying complex reasoning processes. Such reasoning processes typically
exhibit implicitly structured steps. Recent efforts also started investigating
methods to encourage more explicitly structured reasoning procedures to be
captured. In this work, we propose Tab-CoT, a novel tabular-format CoT
prompting method, which allows the complex reasoning process to be explicitly
modelled in a highly structured manner. Despite its simplicity, we show that
our approach is capable of performing reasoning across multiple dimensions
(i.e., both rows and columns). We demonstrate our approach's strong zero-shot
and few-shot capabilities through extensive experiments on a range of reasoning
tasks.
|
[
"cs.CL"
] | false |
2305.17740
|
2023-05-28T14:48:38Z
|
Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot
LLMs
|
[
"Akshay Nambi",
"Vaibhav Balloli",
"Mercy Ranjit",
"Tanuja Ganu",
"Kabir Ahuja",
"Sunayana Sitaram",
"Kalika Bali"
] |
Large language models (LLMs) are at the forefront of transforming numerous
domains globally. However, their inclusivity and effectiveness remain limited
for non-Latin scripts and low-resource languages. This paper tackles the
imperative challenge of enhancing the multilingual performance of LLMs,
specifically focusing on Generative models. Through systematic investigation
and evaluation of diverse languages using popular question-answering (QA)
datasets, we present novel techniques that unlock the true potential of LLMs in
a polyglot landscape. Our approach encompasses three key strategies that yield
remarkable improvements in multilingual proficiency. First, by meticulously
optimizing prompts tailored for polyglot LLMs, we unlock their latent
capabilities, resulting in substantial performance boosts across languages.
Second, we introduce a new hybrid approach that synergizes GPT generation with
multilingual embeddings and achieves significant multilingual performance
improvement on critical tasks like QA and retrieval. Finally, to further propel
the performance of polyglot LLMs, we introduce a novel learning algorithm that
dynamically selects the optimal prompt strategy, LLM model, and embeddings per
query. This dynamic adaptation maximizes the efficacy of LLMs across languages,
outperforming best static and random strategies. Our results show substantial
advancements in multilingual understanding and generation across a diverse
range of languages.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.17817
|
2023-05-28T22:36:35Z
|
Transfer Learning for Power Outage Detection Task with Limited Training
Data
|
[
"Olukunle Owolabi"
] |
Early detection of power outages is crucial for maintaining a reliable power
distribution system. This research investigates the use of transfer learning
and language models in detecting outages with limited labeled data. By
leveraging pretraining and transfer learning, models can generalize to unseen
classes.
Using a curated balanced dataset of social media tweets related to power
outages, we conducted experiments using zero-shot and few-shot learning. Our
hypothesis is that Language Models pretrained with limited data could achieve
high performance in outage detection tasks over baseline models. Results show
that while classical models outperform zero-shot Language Models, few-shot
fine-tuning significantly improves their performance. For example, with 10%
fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5%
accuracy (+8.5%). This has practical implications for analyzing and localizing
outages in scenarios with limited data availability.
Our evaluation provides insights into the potential of few-shot fine-tuning
with Language Models for power outage detection, highlighting their strengths
and limitations. This research contributes to the knowledge base of leveraging
advanced natural language processing techniques for managing critical
infrastructure.
|
[
"cs.CL",
"stat.AP"
] | false |
2305.17826
|
2023-05-28T23:35:17Z
|
NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models
|
[
"Kai Mei",
"Zheng Li",
"Zhenting Wang",
"Yang Zhang",
"Shiqing Ma"
] |
Prompt-based learning is vulnerable to backdoor attacks. Existing backdoor
attacks against prompt-based models consider injecting backdoors into the
entire embedding layers or word embedding vectors. Such attacks can be easily
affected by retraining on downstream tasks and with different prompting
strategies, limiting the transferability of backdoor attacks. In this work, we
propose transferable backdoor attacks against prompt-based models, called
NOTABLE, which is independent of downstream tasks and prompting strategies.
Specifically, NOTABLE injects backdoors into the encoders of PLMs by utilizing
an adaptive verbalizer to bind triggers to specific words (i.e., anchors). It
activates the backdoor by pasting input with triggers to reach
adversary-desired anchors, achieving independence from downstream tasks and
prompting strategies. We conduct experiments on six NLP tasks, three popular
models, and three prompting strategies. Empirical results show that NOTABLE
achieves superior attack performance (i.e., attack success rate over 90% on all
the datasets), and outperforms two state-of-the-art baselines. Evaluations on
three defenses show the robustness of NOTABLE. Our code can be found at
https://github.com/RU-System-Software-and-Security/Notable.
|
[
"cs.CL",
"cs.CR"
] | false |
2306.01768
|
2023-05-28T20:25:20Z
|
A Quantitative Review on Language Model Efficiency Research
|
[
"Meng Jiang",
"Hy Dang",
"Lingbo Tong"
] |
Language models (LMs) are being scaled and becoming powerful. Improving their
efficiency is one of the core research topics in neural information processing
systems. Tay et al. (2022) provided a comprehensive overview of efficient
Transformers that have become an indispensable staple in the field of NLP.
However, in the section of "On Evaluation", they left an open question "which
fundamental efficient Transformer one should consider," answered by "still a
mystery" because "many research papers select their own benchmarks."
Unfortunately, there was not quantitative analysis about the performances of
Transformers on any benchmarks. Moreover, state space models (SSMs) have
demonstrated their abilities of modeling long-range sequences with
non-attention mechanisms, which were not discussed in the prior review. This
article makes a meta analysis on the results from a set of papers on efficient
Transformers as well as those on SSMs. It provides a quantitative review on LM
efficiency research and gives suggestions for future research.
|
[
"cs.LG",
"cs.CL"
] | false |
2305.17619
|
2023-05-28T03:29:59Z
|
AI Coach Assist: An Automated Approach for Call Recommendation in
Contact Centers for Agent Coaching
|
[
"Md Tahmid Rahman Laskar",
"Cheng Chen",
"Xue-Yong Fu",
"Mahsa Azizi",
"Shashi Bhushan",
"Simon Corston-Oliver"
] |
In recent years, the utilization of Artificial Intelligence (AI) in the
contact center industry is on the rise. One area where AI can have a
significant impact is in the coaching of contact center agents. By analyzing
call transcripts using Natural Language Processing (NLP) techniques, it would
be possible to quickly determine which calls are most relevant for coaching
purposes. In this paper, we present AI Coach Assist, which leverages the
pre-trained transformer-based language models to determine whether a given call
is coachable or not based on the quality assurance (QA) questions asked by the
contact center managers or supervisors. The system was trained and evaluated on
a large dataset collected from real-world contact centers and provides an
effective way to recommend calls to the contact center managers that are more
likely to contain coachable moments. Our experimental findings demonstrate the
potential of AI Coach Assist to improve the coaching process, resulting in
enhancing the performance of contact center agents.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.17627
|
2023-05-28T04:25:04Z
|
Robust Natural Language Understanding with Residual Attention Debiasing
|
[
"Fei Wang",
"James Y. Huang",
"Tianyi Yan",
"Wenxuan Zhou",
"Muhao Chen"
] |
Natural language understanding (NLU) models often suffer from unintended
dataset biases. Among bias mitigation methods, ensemble-based debiasing
methods, especially product-of-experts (PoE), have stood out for their
impressive empirical success. However, previous ensemble-based debiasing
methods typically apply debiasing on top-level logits without directly
addressing biased attention patterns. Attention serves as the main media of
feature interaction and aggregation in PLMs and plays a crucial role in
providing robust prediction. In this paper, we propose REsidual Attention
Debiasing (READ), an end-to-end debiasing method that mitigates unintended
biases from attention. Experiments on three NLU tasks show that READ
significantly improves the performance of BERT-based models on OOD data with
shortcuts removed, including +12.9% accuracy on HANS, +11.0% accuracy on
FEVER-Symmetric, and +2.7% F1 on PAWS. Detailed analyses demonstrate the
crucial role of unbiased attention in robust NLU models and that READ
effectively mitigates biases in attention. Code is available at
https://github.com/luka-group/READ.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.17651
|
2023-05-28T07:09:33Z
|
DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech
Models
|
[
"Yifan Peng",
"Yui Sudo",
"Shakeel Muhammad",
"Shinji Watanabe"
] |
Self-supervised learning (SSL) has achieved notable success in many speech
processing tasks, but the large model size and heavy computational cost hinder
the deployment. Knowledge distillation trains a small student model to mimic
the behavior of a large teacher model. However, the student architecture
usually needs to be manually designed and will remain fixed during training,
which requires prior knowledge and can lead to suboptimal performance. Inspired
by recent success of task-specific structured pruning, we propose DPHuBERT, a
novel task-agnostic compression method for speech SSL based on joint
distillation and pruning. Experiments on SUPERB show that DPHuBERT outperforms
pure distillation methods in almost all tasks. Moreover, DPHuBERT requires
little training time and performs well with limited training data, making it
suitable for resource-constrained applications. Our method can also be applied
to various speech SSL models. Our code and models will be publicly available.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.17733
|
2023-05-28T14:15:19Z
|
Investigating Pre-trained Audio Encoders in the Low-Resource Condition
|
[
"Hao Yang",
"Jinming Zhao",
"Gholamreza Haffari",
"Ehsan Shareghi"
] |
Pre-trained speech encoders have been central to pushing state-of-the-art
results across various speech understanding and generation tasks. Nonetheless,
the capabilities of these encoders in low-resource settings are yet to be
thoroughly explored. To address this, we conduct a comprehensive set of
experiments using a representative set of 3 state-of-the-art encoders
(Wav2vec2, WavLM, Whisper) in the low-resource setting across 7 speech
understanding and generation tasks. We provide various quantitative and
qualitative analyses on task performance, convergence speed, and
representational properties of the encoders. We observe a connection between
the pre-training protocols of these encoders and the way in which they capture
information in their internal layers. In particular, we observe the Whisper
encoder exhibits the greatest low-resource capabilities on content-driven tasks
in terms of performance and convergence speed.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.17739
|
2023-05-28T14:46:54Z
|
Range-Based Equal Error Rate for Spoof Localization
|
[
"Lin Zhang",
"Xin Wang",
"Erica Cooper",
"Nicholas Evans",
"Junichi Yamagishi"
] |
Spoof localization, also called segment-level detection, is a crucial task
that aims to locate spoofs in partially spoofed audio. The equal error rate
(EER) is widely used to measure performance for such biometric scenarios.
Although EER is the only threshold-free metric, it is usually calculated in a
point-based way that uses scores and references with a pre-defined temporal
resolution and counts the number of misclassified segments. Such point-based
measurement overly relies on this resolution and may not accurately measure
misclassified ranges. To properly measure misclassified ranges and better
evaluate spoof localization performance, we upgrade point-based EER to
range-based EER. Then, we adapt the binary search algorithm for calculating
range-based EER and compare it with the classical point-based EER. Our analyses
suggest utilizing either range-based EER, or point-based EER with a proper
temporal resolution can fairly and properly evaluate the performance of spoof
localization.
|
[
"cs.SD",
"cs.CL",
"eess.AS"
] | false |
2305.17782
|
2023-05-28T17:48:48Z
|
RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech
Recognition
|
[
"Wei Zhou",
"Eugen Beck",
"Simon Berger",
"Ralf Schlüter",
"Hermann Ney"
] |
Modern public ASR tools usually provide rich support for training various
sequence-to-sequence (S2S) models, but rather simple support for decoding
open-vocabulary scenarios only. For closed-vocabulary scenarios, public tools
supporting lexical-constrained decoding are usually only for classical ASR, or
do not support all S2S models. To eliminate this restriction on research
possibilities such as modeling unit choice, we present RASR2 in this work, a
research-oriented generic S2S decoder implemented in C++. It offers a strong
flexibility/compatibility for various S2S models, language models, label
units/topologies and neural network architectures. It provides efficient
decoding for both open- and closed-vocabulary scenarios based on a generalized
search framework with rich support for different search modes and settings. We
evaluate RASR2 with a wide range of experiments on both switchboard and
Librispeech corpora. Our source code is public online.
|
[
"cs.CL",
"cs.SD",
"eess.AS"
] | false |
2305.18410
|
2023-05-28T17:07:46Z
|
Understanding Breast Cancer Survival: Using Causality and Language
Models on Multi-omics Data
|
[
"Mugariya Farooq",
"Shahad Hardan",
"Aigerim Zhumbhayeva",
"Yujia Zheng",
"Preslav Nakov",
"Kun Zhang"
] |
The need for more usable and explainable machine learning models in
healthcare increases the importance of developing and utilizing causal
discovery algorithms, which aim to discover causal relations by analyzing
observational data. Explainable approaches aid clinicians and biologists in
predicting the prognosis of diseases and suggesting proper treatments. However,
very little research has been conducted at the crossroads between causal
discovery, genomics, and breast cancer, and we aim to bridge this gap.
Moreover, evaluation of causal discovery methods on real data is in general
notoriously difficult because ground-truth causal relations are usually
unknown, and accordingly, in this paper, we also propose to address the
evaluation problem with large language models. In particular, we exploit
suitable causal discovery algorithms to investigate how various perturbations
in the genome can affect the survival of patients diagnosed with breast cancer.
We used three main causal discovery algorithms: PC, Greedy Equivalence Search
(GES), and a Generalized Precision Matrix-based one. We experiment with a
subset of The Cancer Genome Atlas, which contains information about mutations,
copy number variations, protein levels, and gene expressions for 705 breast
cancer patients. Our findings reveal important factors related to the vital
status of patients using causal discovery algorithms. However, the reliability
of these results remains a concern in the medical domain. Accordingly, as
another contribution of the work, the results are validated through language
models trained on biomedical literature, such as BlueBERT and other large
language models trained on medical corpora. Our results profess proper
utilization of causal discovery algorithms and language models for revealing
reliable causal relations for clinical applications.
|
[
"cs.LG",
"cs.CL",
"q-bio.GN",
"stat.ME"
] | false |
2305.18419
|
2023-05-28T19:31:45Z
|
Semantic Segmentation with Bidirectional Language Models Improves
Long-form ASR
|
[
"W. Ronny Huang",
"Hao Zhang",
"Shankar Kumar",
"Shuo-yiin Chang",
"Tara N. Sainath"
] |
We propose a method of segmenting long-form speech by separating semantically
complete sentences within the utterance. This prevents the ASR decoder from
needlessly processing faraway context while also preventing it from missing
relevant context within the current sentence. Semantically complete sentence
boundaries are typically demarcated by punctuation in written text; but
unfortunately, spoken real-world utterances rarely contain punctuation. We
address this limitation by distilling punctuation knowledge from a
bidirectional teacher language model (LM) trained on written, punctuated text.
We compare our segmenter, which is distilled from the LM teacher, against a
segmenter distilled from a acoustic-pause-based teacher used in other works, on
a streaming ASR pipeline. The pipeline with our segmenter achieves a 3.2%
relative WER gain along with a 60 ms median end-of-segment latency reduction on
a YouTube captioning task.
|
[
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2305.17608
|
2023-05-28T02:12:00Z
|
Reward Collapse in Aligning Large Language Models
|
[
"Ziang Song",
"Tianle Cai",
"Jason D. Lee",
"Weijie J. Su"
] |
The extraordinary capabilities of large language models (LLMs) such as
ChatGPT and GPT-4 are in part unleashed by aligning them with reward models
that are trained on human preferences, which are often represented as rankings
of responses to prompts. In this paper, we document the phenomenon of
\textit{reward collapse}, an empirical observation where the prevailing
ranking-based approach results in an \textit{identical} reward distribution
\textit{regardless} of the prompts during the terminal phase of training. This
outcome is undesirable as open-ended prompts like ``write a short story about
your best friend'' should yield a continuous range of rewards for their
completions, while specific prompts like ``what is the capital of New Zealand''
should generate either high or low rewards. Our theoretical investigation
reveals that reward collapse is primarily due to the insufficiency of the
ranking-based objective function to incorporate prompt-related information
during optimization. This insight allows us to derive closed-form expressions
for the reward distribution associated with a set of utility functions in an
asymptotic regime. To overcome reward collapse, we introduce a prompt-aware
optimization scheme that provably admits a prompt-dependent reward distribution
within the interpolating regime. Our experimental results suggest that our
proposed prompt-aware utility functions significantly alleviate reward collapse
during the training of reward models.
|
[
"cs.LG",
"cs.AI",
"cs.CL",
"math.OC",
"stat.ML"
] | false |
2305.17623
|
2023-05-28T03:59:37Z
|
On the Value of Myopic Behavior in Policy Reuse
|
[
"Kang Xu",
"Chenjia Bai",
"Shuang Qiu",
"Haoran He",
"Bin Zhao",
"Zhen Wang",
"Wei Li",
"Xuelong Li"
] |
Leveraging learned strategies in unfamiliar scenarios is fundamental to human
intelligence. In reinforcement learning, rationally reusing the policies
acquired from other tasks or human experts is critical for tackling problems
that are difficult to learn from scratch. In this work, we present a framework
called Selective Myopic bEhavior Control~(SMEC), which results from the insight
that the short-term behaviors of prior policies are sharable across tasks. By
evaluating the behaviors of prior policies via a hybrid value function
architecture, SMEC adaptively aggregates the sharable short-term behaviors of
prior policies and the long-term behaviors of the task policy, leading to
coordinated decisions. Empirical results on a collection of manipulation and
locomotion tasks demonstrate that SMEC outperforms existing methods, and
validate the ability of SMEC to leverage related prior policies.
|
[
"cs.LG"
] | false |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.