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2305.17221
|
2023-05-26T19:25:49Z
|
Federated Learning for Semantic Parsing: Task Formulation, Evaluation
Setup, New Algorithms
|
[
"Tianshu Zhang",
"Changchang Liu",
"Wei-Han Lee",
"Yu Su",
"Huan Sun"
] |
This paper studies a new task of federated learning (FL) for semantic
parsing, where multiple clients collaboratively train one global model without
sharing their semantic parsing data. By leveraging data from multiple clients,
the FL paradigm can be especially beneficial for clients that have little
training data to develop a data-hungry neural semantic parser on their own. We
propose an evaluation setup to study this task, where we re-purpose widely-used
single-domain text-to-SQL datasets as clients to form a realistic heterogeneous
FL setting and collaboratively train a global model. As standard FL algorithms
suffer from the high client heterogeneity in our realistic setup, we further
propose a novel LOss Reduction Adjusted Re-weighting (Lorar) mechanism to
mitigate the performance degradation, which adjusts each client's contribution
to the global model update based on its training loss reduction during each
round. Our intuition is that the larger the loss reduction, the further away
the current global model is from the client's local optimum, and the larger
weight the client should get. By applying Lorar to three widely adopted FL
algorithms (FedAvg, FedOPT and FedProx), we observe that their performance can
be improved substantially on average (4%-20% absolute gain under MacroAvg) and
that clients with smaller datasets enjoy larger performance gains. In addition,
the global model converges faster for almost all the clients.
|
[
"cs.CL",
"cs.AI",
"cs.DB",
"cs.LG"
] | false |
2305.17306
|
2023-05-26T23:46:42Z
|
Chain-of-Thought Hub: A Continuous Effort to Measure Large Language
Models' Reasoning Performance
|
[
"Yao Fu",
"Litu Ou",
"Mingyu Chen",
"Yuhao Wan",
"Hao Peng",
"Tushar Khot"
] |
As large language models (LLMs) are continuously being developed, their
evaluation becomes increasingly important yet challenging. This work proposes
Chain-of-Thought Hub, an open-source evaluation suite on the multi-step
reasoning capabilities of large language models. We are interested in this
setting for two reasons: (1) from the behavior of GPT and PaLM model family, we
observe that complex reasoning is likely to be a key differentiator between
weaker and stronger LLMs; (2) we envisage large language models to become the
next-generation computational platform and foster an ecosystem of LLM-based new
applications, this naturally requires the foundation models to perform complex
tasks that often involve the composition of linguistic and logical operations.
Our approach is to compile a suite of challenging reasoning benchmarks to track
the progress of LLMs. Our current results show that: (1) model scale clearly
correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and
PaLM-2 are the only two models that are comparable with GPT-4, while
open-sourced models still lag behind; (3) LLaMA-65B performs closely to
code-davinci-002, indicating that with successful further development such as
reinforcement learning from human feedback (RLHF), it has great potential to be
close to GPT-3.5-Turbo. Our results also suggest that for the open-source
efforts to catch up, the community may focus more on building better base
models and exploring RLHF.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | true |
2305.18350
|
2023-05-26T11:51:31Z
|
Towards Open-World Product Attribute Mining: A Lightly-Supervised
Approach
|
[
"Liyan Xu",
"Chenwei Zhang",
"Xian Li",
"Jingbo Shang",
"Jinho D. Choi"
] |
We present a new task setting for attribute mining on e-commerce products,
serving as a practical solution to extract open-world attributes without
extensive human intervention. Our supervision comes from a high-quality seed
attribute set bootstrapped from existing resources, and we aim to expand the
attribute vocabulary of existing seed types, and also to discover any new
attribute types automatically. A new dataset is created to support our setting,
and our approach Amacer is proposed specifically to tackle the limited
supervision. Especially, given that no direct supervision is available for
those unseen new attributes, our novel formulation exploits self-supervised
heuristic and unsupervised latent attributes, which attains implicit semantic
signals as additional supervision by leveraging product context. Experiments
suggest that our approach surpasses various baselines by 12 F1, expanding
attributes of existing types significantly by up to 12 times, and discovering
values from 39% new types.
|
[
"cs.LG",
"cs.CL",
"cs.IR"
] | false |
2305.18357
|
2023-05-26T18:05:57Z
|
DeepSI: Interactive Deep Learning for Semantic Interaction
|
[
"Yali Bian",
"Chris North"
] |
In this paper, we design novel interactive deep learning methods to improve
semantic interactions in visual analytics applications. The ability of semantic
interaction to infer analysts' precise intents during sensemaking is dependent
on the quality of the underlying data representation. We propose the
$\text{DeepSI}_{\text{finetune}}$ framework that integrates deep learning into
the human-in-the-loop interactive sensemaking pipeline, with two important
properties. First, deep learning extracts meaningful representations from raw
data, which improves semantic interaction inference. Second, semantic
interactions are exploited to fine-tune the deep learning representations,
which then further improves semantic interaction inference. This feedback loop
between human interaction and deep learning enables efficient learning of user-
and task-specific representations. To evaluate the advantage of embedding the
deep learning within the semantic interaction loop, we compare
$\text{DeepSI}_{\text{finetune}}$ against a state-of-the-art but more basic use
of deep learning as only a feature extractor pre-processed outside of the
interactive loop. Results of two complementary studies, a human-centered
qualitative case study and an algorithm-centered simulation-based quantitative
experiment, show that $\text{DeepSI}_{\text{finetune}}$ more accurately
captures users' complex mental models with fewer interactions.
|
[
"cs.LG",
"cs.AI",
"cs.CL",
"cs.HC"
] | false |
2305.18620
|
2023-05-26T00:53:18Z
|
CONA: A novel CONtext-Aware instruction paradigm for communication using
large language model
|
[
"Nan Zhou",
"Xinghui Tao",
"Xi Chen"
] |
We introduce CONA, a novel context-aware instruction paradigm for effective
knowledge dissemination using generative pre-trained transformer (GPT) models.
CONA is a flexible framework designed to leverage the capabilities of Large
Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge,
Wisdom) hierarchy to automatically instruct and optimise presentation content,
anticipate potential audience inquiries, and provide context-aware answers that
adaptive to the knowledge level of the audience group. The unique aspect of the
CONA paradigm lies in its combination of an independent advisory mechanism and
a recursive feedback loop rooted on the DIKW hierarchy. This synergy
significantly enhances context-aware contents, ensuring they are accessible and
easily comprehended by the audience. This paradigm is an early pioneer to
explore new methods for knowledge dissemination and communication in the LLM
era, offering effective support for everyday knowledge sharing scenarios. We
conduct experiments on a range of audience roles, along with materials from
various disciplines using GPT4. Both quantitative and qualitative results
demonstrated that the proposed CONA paradigm achieved remarkable performance
compared to the outputs guided by conventional prompt engineering.
|
[
"cs.CL",
"cs.AI",
"cs.HC"
] | false |
2306.01761
|
2023-05-26T09:27:43Z
|
Distinguishing Human Generated Text From ChatGPT Generated Text Using
Machine Learning
|
[
"Niful Islam",
"Debopom Sutradhar",
"Humaira Noor",
"Jarin Tasnim Raya",
"Monowara Tabassum Maisha",
"Dewan Md Farid"
] |
ChatGPT is a conversational artificial intelligence that is a member of the
generative pre-trained transformer of the large language model family. This
text generative model was fine-tuned by both supervised learning and
reinforcement learning so that it can produce text documents that seem to be
written by natural intelligence. Although there are numerous advantages of this
generative model, it comes with some reasonable concerns as well. This paper
presents a machine learning-based solution that can identify the ChatGPT
delivered text from the human written text along with the comparative analysis
of a total of 11 machine learning and deep learning algorithms in the
classification process. We have tested the proposed model on a Kaggle dataset
consisting of 10,000 texts out of which 5,204 texts were written by humans and
collected from news and social media. On the corpus generated by GPT-3.5, the
proposed algorithm presents an accuracy of 77%.
|
[
"cs.CL",
"cs.AI",
"cs.LG"
] | false |
2305.16554
|
2023-05-26T00:43:02Z
|
Emergent Agentic Transformer from Chain of Hindsight Experience
|
[
"Hao Liu",
"Pieter Abbeel"
] |
Large transformer models powered by diverse data and model scale have
dominated natural language modeling and computer vision and pushed the frontier
of multiple AI areas. In reinforcement learning (RL), despite many efforts into
transformer-based policies, a key limitation, however, is that current
transformer-based policies cannot learn by directly combining information from
multiple sub-optimal trials. In this work, we address this issue using recently
proposed chain of hindsight to relabel experience, where we train a transformer
on a sequence of trajectory experience ascending sorted according to their
total rewards. Our method consists of relabelling target return of each
trajectory to the maximum total reward among in sequence of trajectories and
training an autoregressive model to predict actions conditioning on past
states, actions, rewards, target returns, and task completion tokens, the
resulting model, Agentic Transformer (AT), can learn to improve upon itself
both at training and test time. As we show on D4RL and ExoRL benchmarks, to the
best our knowledge, this is the first time that a simple transformer-based
model performs competitively with both temporal-difference and
imitation-learning-based approaches, even from sub-optimal data. Our Agentic
Transformer also shows a promising scaling trend that bigger models
consistently improve results.
|
[
"cs.LG"
] | false |
2305.16593
|
2023-05-26T02:51:39Z
|
A Multi-Resolution Physics-Informed Recurrent Neural Network:
Formulation and Application to Musculoskeletal Systems
|
[
"Karan Taneja",
"Xiaolong He",
"Qizhi He",
"J. S. Chen"
] |
This work presents a multi-resolution physics-informed recurrent neural
network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK)
motion and parameter identification of the MSK systems. The MSK application was
selected as the model problem due to its challenging nature in mapping the
high-frequency surface electromyography (sEMG) signals to the low-frequency
body joint motion controlled by the MSK and muscle contraction dynamics. The
proposed method utilizes the fast wavelet transform to decompose the mixed
frequency input sEMG and output joint motion signals into nested
multi-resolution signals. The prediction model is subsequently trained on
coarser-scale input-output signals using a gated recurrent unit (GRU), and then
the trained parameters are transferred to the next level of training with
finer-scale signals. These training processes are repeated recursively under a
transfer-learning fashion until the full-scale training (i.e., with unfiltered
signals) is achieved, while satisfying the underlying dynamic equilibrium.
Numerical examples on recorded subject data demonstrate the effectiveness of
the proposed framework in generating a physics-informed forward-dynamics
surrogate, which yields higher accuracy in motion predictions of elbow
flexion-extension of an MSK system compared to the case with single-scale
training. The framework is also capable of identifying muscle parameters that
are physiologically consistent with the subject's kinematics data.
|
[
"cs.LG"
] | false |
2305.16639
|
2023-05-26T05:28:10Z
|
Universal Approximation and the Topological Neural Network
|
[
"Michael A. Kouritzin",
"Daniel Richard"
] |
A topological neural network (TNN), which takes data from a Tychonoff
topological space instead of the usual finite dimensional space, is introduced.
As a consequence, a distributional neural network (DNN) that takes Borel
measures as data is also introduced. Combined these new neural networks
facilitate things like recognizing long range dependence, heavy tails and other
properties in stochastic process paths or like acting on belief states produced
by particle filtering or hidden Markov model algorithms. The veracity of the
TNN and DNN are then established herein by a strong universal approximation
theorem for Tychonoff spaces and its corollary for spaces of measures. These
theorems show that neural networks can arbitrarily approximate uniformly
continuous functions (with respect to the sup metric) associated with a unique
uniformity. We also provide some discussion showing that neural networks on
positive-finite measures are a generalization of the recent deep learning
notion of deep sets.
|
[
"cs.LG",
"41-02"
] | false |
2305.16683
|
2023-05-26T07:05:08Z
|
Future-conditioned Unsupervised Pretraining for Decision Transformer
|
[
"Zhihui Xie",
"Zichuan Lin",
"Deheng Ye",
"Qiang Fu",
"Wei Yang",
"Shuai Li"
] |
Recent research in offline reinforcement learning (RL) has demonstrated that
return-conditioned supervised learning is a powerful paradigm for
decision-making problems. While promising, return conditioning is limited to
training data labeled with rewards and therefore faces challenges in learning
from unsupervised data. In this work, we aim to utilize generalized future
conditioning to enable efficient unsupervised pretraining from reward-free and
sub-optimal offline data. We propose Pretrained Decision Transformer (PDT), a
conceptually simple approach for unsupervised RL pretraining. PDT leverages
future trajectory information as a privileged context to predict actions during
training. The ability to make decisions based on both present and future
factors enhances PDT's capability for generalization. Besides, this feature can
be easily incorporated into a return-conditioned framework for online
finetuning, by assigning return values to possible futures and sampling future
embeddings based on their respective values. Empirically, PDT outperforms or
performs on par with its supervised pretraining counterpart, especially when
dealing with sub-optimal data. Further analysis reveals that PDT can extract
diverse behaviors from offline data and controllably sample high-return
behaviors by online finetuning. Code is available at here.
|
[
"cs.LG"
] | false |
2305.16691
|
2023-05-26T07:24:24Z
|
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
|
[
"Benjamin Walker",
"Felix Krones",
"Ivan Kiskin",
"Guy Parsons",
"Terry Lyons",
"Adam Mahdi"
] |
This study presents our team PathToMyHeart's contribution to the George B.
Moody PhysioNet Challenge 2022. Two models are implemented. The first model is
a Dual Bayesian ResNet (DBRes), where each patient's recording is segmented
into overlapping log mel spectrograms. These undergo two binary
classifications: present versus unknown or absent, and unknown versus present
or absent. The classifications are aggregated to give a patient's final
classification. The second model is the output of DBRes integrated with
demographic data and signal features using XGBoost.DBRes achieved our best
weighted accuracy of $0.771$ on the hidden test set for murmur classification,
which placed us fourth for the murmur task. (On the clinical outcome task,
which we neglected, we scored 17th with costs of $12637$.) On our held-out
subset of the training set, integrating the demographic data and signal
features improved DBRes's accuracy from $0.762$ to $0.820$. However, this
decreased DBRes's weighted accuracy from $0.780$ to $0.749$. Our results
demonstrate that log mel spectrograms are an effective representation of heart
sound recordings, Bayesian networks provide strong supervised classification
performance, and treating the ternary classification as two binary
classifications increases performance on the weighted accuracy.
|
[
"cs.LG"
] | false |
2305.16777
|
2023-05-26T09:39:36Z
|
Unleashing the Potential of Unsupervised Deep Outlier Detection through
Automated Training Stopping
|
[
"Yihong Huang",
"Yuang Zhang",
"Liping Wang",
"Xuemin Lin"
] |
Outlier detection (OD) has received continuous research interests due to its
wide applications. With the development of deep learning, increasingly deep OD
algorithms are proposed. Despite the availability of numerous deep OD models,
existing research has reported that the performance of deep models is extremely
sensitive to the configuration of hyperparameters (HPs). However, the selection
of HPs for deep OD models remains a notoriously difficult task due to the lack
of any labels and long list of HPs. In our study. we shed light on an essential
factor, training time, that can introduce significant variation in the
performance of deep model. Even the performance is stable across other HPs,
training time itself can cause a serious HP sensitivity issue. Motivated by
this finding, we are dedicated to formulating a strategy to terminate model
training at the optimal iteration. Specifically, we propose a novel metric
called loss entropy to internally evaluate the model performance during
training while an automated training stopping algorithm is devised. To our
knowledge, our approach is the first to enable reliable identification of the
optimal training iteration during training without requiring any labels. Our
experiments on tabular, image datasets show that our approach can be applied to
diverse deep models and datasets. It not only enhances the robustness of deep
models to their HPs, but also improves the performance and reduces plenty of
training time compared to naive training.
|
[
"cs.LG"
] | false |
2305.16971
|
2023-05-26T14:26:36Z
|
Theoretical and Practical Perspectives on what Influence Functions Do
|
[
"Andrea Schioppa",
"Katja Filippova",
"Ivan Titov",
"Polina Zablotskaia"
] |
Influence functions (IF) have been seen as a technique for explaining model
predictions through the lens of the training data. Their utility is assumed to
be in identifying training examples "responsible" for a prediction so that, for
example, correcting a prediction is possible by intervening on those examples
(removing or editing them) and retraining the model. However, recent empirical
studies have shown that the existing methods of estimating IF predict the
leave-one-out-and-retrain effect poorly.
In order to understand the mismatch between the theoretical promise and the
practical results, we analyse five assumptions made by IF methods which are
problematic for modern-scale deep neural networks and which concern convexity,
numeric stability, training trajectory and parameter divergence. This allows us
to clarify what can be expected theoretically from IF. We show that while most
assumptions can be addressed successfully, the parameter divergence poses a
clear limitation on the predictive power of IF: influence fades over training
time even with deterministic training. We illustrate this theoretical result
with BERT and ResNet models.
Another conclusion from the theoretical analysis is that IF are still useful
for model debugging and correcting even though some of the assumptions made in
prior work do not hold: using natural language processing and computer vision
tasks, we verify that mis-predictions can be successfully corrected by taking
only a few fine-tuning steps on influential examples.
|
[
"cs.LG"
] | false |
2305.17094
|
2023-05-26T17:06:15Z
|
Benchmarking state-of-the-art gradient boosting algorithms for
classification
|
[
"Piotr Florek",
"Adam Zagdański"
] |
This work explores the use of gradient boosting in the context of
classification. Four popular implementations, including original GBM algorithm
and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost,
LightGBM and CatBoost), have been thoroughly compared on several publicly
available real-world datasets of sufficient diversity. In the study, special
emphasis was placed on hyperparameter optimization, specifically comparing two
tuning strategies, i.e. randomized search and Bayesian optimization using the
Tree-stuctured Parzen Estimator. The performance of considered methods was
investigated in terms of common classification accuracy metrics as well as
runtime and tuning time. Additionally, obtained results have been validated
using appropriate statistical testing. An attempt was made to indicate a
gradient boosting variant showing the right balance between effectiveness,
reliability and ease of use.
|
[
"cs.LG",
"62H30"
] | false |
2305.17109
|
2023-05-26T17:18:14Z
|
Reinforcement Learning with Simple Sequence Priors
|
[
"Tankred Saanum",
"Noémi Éltető",
"Peter Dayan",
"Marcel Binz",
"Eric Schulz"
] |
Everything else being equal, simpler models should be preferred over more
complex ones. In reinforcement learning (RL), simplicity is typically
quantified on an action-by-action basis -- but this timescale ignores temporal
regularities, like repetitions, often present in sequential strategies. We
therefore propose an RL algorithm that learns to solve tasks with sequences of
actions that are compressible. We explore two possible sources of simple action
sequences: Sequences that can be learned by autoregressive models, and
sequences that are compressible with off-the-shelf data compression algorithms.
Distilling these preferences into sequence priors, we derive a novel
information-theoretic objective that incentivizes agents to learn policies that
maximize rewards while conforming to these priors. We show that the resulting
RL algorithm leads to faster learning, and attains higher returns than
state-of-the-art model-free approaches in a series of continuous control tasks
from the DeepMind Control Suite. These priors also produce a powerful
information-regularized agent that is robust to noisy observations and can
perform open-loop control.
|
[
"cs.LG"
] | false |
2305.17244
|
2023-05-26T20:17:18Z
|
Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks
|
[
"Ketaki Joshi",
"Raghavendra Pradyumna Pothukuchi",
"Andre Wibisono",
"Abhishek Bhattacharjee"
] |
Continual learning on sequential data is critical for many machine learning
(ML) deployments. Unfortunately, LSTM networks, which are commonly used to
learn on sequential data, suffer from catastrophic forgetting and are limited
in their ability to learn multiple tasks continually. We discover that
catastrophic forgetting in LSTM networks can be overcome in two novel and
readily-implementable ways -- separating the LSTM memory either for each task
or for each target label. Our approach eschews the need for explicit
regularization, hypernetworks, and other complex methods. We quantify the
benefits of our approach on recently-proposed LSTM networks for computer memory
access prefetching, an important sequential learning problem in ML-based
computer system optimization. Compared to state-of-the-art weight
regularization methods to mitigate catastrophic forgetting, our approach is
simple, effective, and enables faster learning. We also show that our proposal
enables the use of small, non-regularized LSTM networks for complex natural
language processing in the offline learning scenario, which was previously
considered difficult.
|
[
"cs.LG"
] | false |
2307.11684
|
2023-05-26T02:00:44Z
|
Minibatching Offers Improved Generalization Performance for Second Order
Optimizers
|
[
"Eric Silk",
"Swarnita Chakraborty",
"Nairanjana Dasgupta",
"Anand D. Sarwate",
"Andrew Lumsdaine",
"Tony Chiang"
] |
Training deep neural networks (DNNs) used in modern machine learning is
computationally expensive. Machine learning scientists, therefore, rely on
stochastic first-order methods for training, coupled with significant
hand-tuning, to obtain good performance. To better understand performance
variability of different stochastic algorithms, including second-order methods,
we conduct an empirical study that treats performance as a response variable
across multiple training sessions of the same model. Using 2-factor Analysis of
Variance (ANOVA) with interactions, we show that batch size used during
training has a statistically significant effect on the peak accuracy of the
methods, and that full batch largely performed the worst. In addition, we found
that second-order optimizers (SOOs) generally exhibited significantly lower
variance at specific batch sizes, suggesting they may require less
hyperparameter tuning, leading to a reduced overall time to solution for model
training.
|
[
"cs.LG"
] | false |
2305.16614
|
2023-05-26T04:20:02Z
|
Physical Deep Reinforcement Learning: Safety and Unknown Unknowns
|
[
"Hongpeng Cao",
"Yanbing Mao",
"Lui Sha",
"Marco Caccamo"
] |
In this paper, we propose the Phy-DRL: a physics-model-regulated deep
reinforcement learning framework for safety-critical autonomous systems. The
Phy-DRL is unique in three innovations: i) proactive unknown-unknowns training,
ii) conjunctive residual control (i.e., integration of data-driven control and
physics-model-based control) and safety- \& stability-sensitive reward, and
iii) physics-model-based neural network editing, including link editing and
activation editing. Thanks to the concurrent designs, the Phy-DRL is able to 1)
tolerate unknown-unknowns disturbances, 2) guarantee mathematically provable
safety and stability, and 3) strictly comply with physical knowledge pertaining
to Bellman equation and reward. The effectiveness of the Phy-DRL is finally
validated by an inverted pendulum and a quadruped robot. The experimental
results demonstrate that compared with purely data-driven DRL, Phy-DRL features
remarkably fewer learning parameters, accelerated training and enlarged reward,
while offering enhanced model robustness and safety assurance.
|
[
"cs.AI",
"cs.LG"
] | false |
2305.16703
|
2023-05-26T07:44:19Z
|
Sources of Uncertainty in Machine Learning -- A Statisticians' View
|
[
"Cornelia Gruber",
"Patrick Oliver Schenk",
"Malte Schierholz",
"Frauke Kreuter",
"Göran Kauermann"
] |
Machine Learning and Deep Learning have achieved an impressive standard
today, enabling us to answer questions that were inconceivable a few years ago.
Besides these successes, it becomes clear, that beyond pure prediction, which
is the primary strength of most supervised machine learning algorithms, the
quantification of uncertainty is relevant and necessary as well. While first
concepts and ideas in this direction have emerged in recent years, this paper
adopts a conceptual perspective and examines possible sources of uncertainty.
By adopting the viewpoint of a statistician, we discuss the concepts of
aleatoric and epistemic uncertainty, which are more commonly associated with
machine learning. The paper aims to formalize the two types of uncertainty and
demonstrates that sources of uncertainty are miscellaneous and can not always
be decomposed into aleatoric and epistemic. Drawing parallels between
statistical concepts and uncertainty in machine learning, we also demonstrate
the role of data and their influence on uncertainty.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.16704
|
2023-05-26T07:47:21Z
|
A Closer Look at In-Context Learning under Distribution Shifts
|
[
"Kartik Ahuja",
"David Lopez-Paz"
] |
In-context learning, a capability that enables a model to learn from input
examples on the fly without necessitating weight updates, is a defining
characteristic of large language models. In this work, we follow the setting
proposed in (Garg et al., 2022) to better understand the generality and
limitations of in-context learning from the lens of the simple yet fundamental
task of linear regression. The key question we aim to address is: Are
transformers more adept than some natural and simpler architectures at
performing in-context learning under varying distribution shifts? To compare
transformers, we propose to use a simple architecture based on set-based
Multi-Layer Perceptrons (MLPs). We find that both transformers and set-based
MLPs exhibit in-context learning under in-distribution evaluations, but
transformers more closely emulate the performance of ordinary least squares
(OLS). Transformers also display better resilience to mild distribution shifts,
where set-based MLPs falter. However, under severe distribution shifts, both
models' in-context learning abilities diminish.
|
[
"cs.LG",
"stat.ML"
] | true |
2305.16808
|
2023-05-26T10:42:21Z
|
Geometric deep learning approach to knot theory
|
[
"Lennart Jaretzki"
] |
In this paper, we introduce a novel way to use geometric deep learning for
knot data by constructing a functor that takes knots to graphs and using graph
neural networks. We will attempt to predict several knot invariants with this
approach. This approach demonstrates high generalization capabilities.
|
[
"math.GT",
"cs.LG",
"57K10"
] | false |
2305.16849
|
2023-05-26T12:00:37Z
|
Green Runner: A tool for efficient model selection from model
repositories
|
[
"Jai Kannan",
"Scott Barnett",
"Anj Simmons",
"Taylan Selvi",
"Luis Cruz"
] |
Deep learning models have become essential in software engineering, enabling
intelligent features like image captioning and document generation. However,
their popularity raises concerns about environmental impact and inefficient
model selection. This paper introduces GreenRunnerGPT, a novel tool for
efficiently selecting deep learning models based on specific use cases. It
employs a large language model to suggest weights for quality indicators,
optimizing resource utilization. The tool utilizes a multi-armed bandit
framework to evaluate models against target datasets, considering tradeoffs. We
demonstrate that GreenRunnerGPT is able to identify a model suited to a target
use case without wasteful computations that would occur under a brute-force
approach to model selection.
|
[
"cs.SE",
"cs.LG"
] | false |
2305.16864
|
2023-05-26T12:15:56Z
|
Knowledge Extraction with Interval Temporal Logic Decision Trees
|
[
"Guido Sciavicco",
"Stan Ionel Eduard"
] |
Multivariate temporal, or time, series classification is, in a way, the
temporal generalization of (numeric) classification, as every instance is
described by multiple time series instead of multiple values. Symbolic
classification is the machine learning strategy to extract explicit knowledge
from a data set, and the problem of symbolic classification of multivariate
temporal series requires the design, implementation, and test of ad-hoc machine
learning algorithms, such as, for example, algorithms for the extraction of
temporal versions of decision trees. One of the most well-known algorithms for
decision tree extraction from categorical data is Quinlan's ID3, which was
later extended to deal with numerical attributes, resulting in an algorithm
known as C4.5, and implemented in many open-sources data mining libraries,
including the so-called Weka, which features an implementation of C4.5 called
J48. ID3 was recently generalized to deal with temporal data in form of
timelines, which can be seen as discrete (categorical) versions of multivariate
time series, and such a generalization, based on the interval temporal logic
HS, is known as Temporal ID3. In this paper we introduce Temporal C4.5, that
allows the extraction of temporal decision trees from undiscretized
multivariate time series, describe its implementation, called Temporal J48, and
discuss the outcome of a set of experiments with the latter on a collection of
public data sets, comparing the results with those obtained by other,
classical, multivariate time series classification methods.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.16886
|
2023-05-26T12:45:58Z
|
Peeking inside Sparse Neural Networks using Multi-Partite Graph
Representations
|
[
"Elia Cunegatti",
"Doina Bucur",
"Giovanni Iacca"
] |
Modern Deep Neural Networks (DNNs) have achieved very high performance at the
expense of computational resources. To decrease the computational burden,
several techniques have proposed to extract, from a given DNN, efficient
subnetworks which are able to preserve performance while reducing the number of
network parameters. The literature provides a broad set of techniques to
discover such subnetworks, but few works have studied the peculiar topologies
of such pruned architectures. In this paper, we propose a novel \emph{unrolled
input-aware} bipartite Graph Encoding (GE) that is able to generate, for each
layer in an either sparse or dense neural network, its corresponding graph
representation based on its relation with the input data. We also extend it
into a multipartite GE, to capture the relation between layers. Then, we
leverage on topological properties to study the difference between the existing
pruning algorithms and algorithm categories, as well as the relation between
topologies and performance.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.16890
|
2023-05-26T12:51:16Z
|
Universal Weak Coreset
|
[
"Ragesh Jaiswal",
"Amit Kumar"
] |
Coresets for $k$-means and $k$-median problems yield a small summary of the
data, which preserve the clustering cost with respect to any set of $k$
centers. Recently coresets have also been constructed for constrained $k$-means
and $k$-median problems. However, the notion of coresets has the drawback that
(i) they can only be applied in settings where the input points are allowed to
have weights, and (ii) in general metric spaces, the size of the coresets can
depend logarithmically on the number of points. The notion of weak coresets,
which have less stringent requirements than coresets, has been studied in the
context of classical $k$-means and $k$-median problems. A weak coreset is a
pair $(J,S)$ of subsets of points, where $S$ acts as a summary of the point set
and $J$ as a set of potential centers. This pair satisfies the properties that
(i) $S$ is a good summary of the data as long as the $k$ centers are chosen
from $J$ only, and (ii) there is a good choice of $k$ centers in $J$ with cost
close to the optimal cost. We develop this framework, which we call universal
weak coresets, for constrained clustering settings. In conjunction with recent
coreset constructions for constrained settings, our designs give greater data
compression, are conceptually simpler, and apply to a wide range of constrained
$k$-median and $k$-means problems.
|
[
"cs.DS",
"cs.LG"
] | false |
2305.16948
|
2023-05-26T14:00:35Z
|
Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets
|
[
"Hayeon Lee",
"Sohyun An",
"Minseon Kim",
"Sung Ju Hwang"
] |
Distillation-aware Neural Architecture Search (DaNAS) aims to search for an
optimal student architecture that obtains the best performance and/or
efficiency when distilling the knowledge from a given teacher model. Previous
DaNAS methods have mostly tackled the search for the neural architecture for
fixed datasets and the teacher, which are not generalized well on a new task
consisting of an unseen dataset and an unseen teacher, thus need to perform a
costly search for any new combination of the datasets and the teachers. For
standard NAS tasks without KD, meta-learning-based computationally efficient
NAS methods have been proposed, which learn the generalized search process over
multiple tasks (datasets) and transfer the knowledge obtained over those tasks
to a new task. However, since they assume learning from scratch without KD from
a teacher, they might not be ideal for DaNAS scenarios. To eliminate the
excessive computational cost of DaNAS methods and the sub-optimality of rapid
NAS methods, we propose a distillation-aware meta accuracy prediction model,
DaSS (Distillation-aware Student Search), which can predict a given
architecture's final performances on a dataset when performing KD with a given
teacher, without having actually to train it on the target task. The
experimental results demonstrate that our proposed meta-prediction model
successfully generalizes to multiple unseen datasets for DaNAS tasks, largely
outperforming existing meta-NAS methods and rapid NAS baselines. Code is
available at https://github.com/CownowAn/DaSS
|
[
"cs.LG",
"cs.AI"
] | false |
2305.16998
|
2023-05-26T14:58:30Z
|
A Tale of Two Approximations: Tightening Over-Approximation for DNN
Robustness Verification via Under-Approximation
|
[
"Zhiyi Xue",
"Si Liu",
"Zhaodi Zhang",
"Yiting Wu",
"Min Zhang"
] |
The robustness of deep neural networks (DNNs) is crucial to the hosting
system's reliability and security. Formal verification has been demonstrated to
be effective in providing provable robustness guarantees. To improve its
scalability, over-approximating the non-linear activation functions in DNNs by
linear constraints has been widely adopted, which transforms the verification
problem into an efficiently solvable linear programming problem. Many efforts
have been dedicated to defining the so-called tightest approximations to reduce
overestimation imposed by over-approximation. In this paper, we study existing
approaches and identify a dominant factor in defining tight approximation,
namely the approximation domain of the activation function. We find out that
tight approximations defined on approximation domains may not be as tight as
the ones on their actual domains, yet existing approaches all rely only on
approximation domains. Based on this observation, we propose a novel
dual-approximation approach to tighten over-approximations, leveraging an
activation function's underestimated domain to define tight approximation
bounds. We implement our approach with two complementary algorithms based
respectively on Monte Carlo simulation and gradient descent into a tool called
DualApp. We assess it on a comprehensive benchmark of DNNs with different
architectures. Our experimental results show that DualApp significantly
outperforms the state-of-the-art approaches with 100% - 1000% improvement on
the verified robustness ratio and 10.64% on average (up to 66.53%) on the
certified lower bound.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.17063
|
2023-05-26T16:19:26Z
|
Vecchia Gaussian Process Ensembles on Internal Representations of Deep
Neural Networks
|
[
"Felix Jimenez",
"Matthias Katzfuss"
] |
For regression tasks, standard Gaussian processes (GPs) provide natural
uncertainty quantification, while deep neural networks (DNNs) excel at
representation learning. We propose to synergistically combine these two
approaches in a hybrid method consisting of an ensemble of GPs built on the
output of hidden layers of a DNN. GP scalability is achieved via Vecchia
approximations that exploit nearest-neighbor conditional independence. The
resulting deep Vecchia ensemble not only imbues the DNN with uncertainty
quantification but can also provide more accurate and robust predictions. We
demonstrate the utility of our model on several datasets and carry out
experiments to understand the inner workings of the proposed method.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.17149
|
2023-05-26T05:31:23Z
|
Diagnostic Spatio-temporal Transformer with Faithful Encoding
|
[
"Jokin Labaien",
"Tsuyoshi Idé",
"Pin-Yu Chen",
"Ekhi Zugasti",
"Xabier De Carlos"
] |
This paper addresses the task of anomaly diagnosis when the underlying data
generation process has a complex spatio-temporal (ST) dependency. The key
technical challenge is to extract actionable insights from the dependency
tensor characterizing high-order interactions among temporal and spatial
indices. We formalize the problem as supervised dependency discovery, where the
ST dependency is learned as a side product of multivariate time-series
classification. We show that temporal positional encoding used in existing ST
transformer works has a serious limitation in capturing higher frequencies
(short time scales). We propose a new positional encoding with a theoretical
guarantee, based on discrete Fourier transform. We also propose a new ST
dependency discovery framework, which can provide readily consumable diagnostic
information in both spatial and temporal directions. Finally, we demonstrate
the utility of the proposed model, DFStrans (Diagnostic Fourier-based
Spatio-temporal Transformer), in a real industrial application of building
elevator control.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.17156
|
2023-05-26T16:40:44Z
|
An Improved Model Ensembled of Different Hyper-parameter Tuned Machine
Learning Algorithms for Fetal Health Prediction
|
[
"Md. Simul Hasan Talukder",
"Sharmin Akter"
] |
Fetal health is a critical concern during pregnancy as it can impact the
well-being of both the mother and the baby. Regular monitoring and timely
interventions are necessary to ensure the best possible outcomes. While there
are various methods to monitor fetal health in the mother's womb, the use of
artificial intelligence (AI) can improve the accuracy, efficiency, and speed of
diagnosis. In this study, we propose a robust ensemble model called ensemble of
tuned Support Vector Machine and ExtraTrees (ETSE) for predicting fetal health.
Initially, we employed various data preprocessing techniques such as outlier
rejection, missing value imputation, data standardization, and data sampling.
Then, seven machine learning (ML) classifiers including Support Vector Machine
(SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Decision Tree
(DT), Random Forest (RF), ExtraTrees (ET), and K-Neighbors were implemented.
These models were evaluated and then optimized by hyperparameter tuning using
the grid search technique. Finally, we analyzed the performance of our proposed
ETSE model. The performance analysis of each model revealed that our proposed
ETSE model outperformed the other models with 100% precision, 100% recall, 100%
F1-score, and 99.66% accuracy. This indicates that the ETSE model can
effectively predict fetal health, which can aid in timely interventions and
improve outcomes for both the mother and the baby.
|
[
"cs.LG",
"cs.AI",
"14J60 (Primary) 14F05, 14J26 (Secondary)",
"I.2.10"
] | false |
2305.17250
|
2023-05-26T20:37:06Z
|
Self-Supervised Reinforcement Learning that Transfers using Random
Features
|
[
"Boyuan Chen",
"Chuning Zhu",
"Pulkit Agrawal",
"Kaiqing Zhang",
"Abhishek Gupta"
] |
Model-free reinforcement learning algorithms have exhibited great potential
in solving single-task sequential decision-making problems with
high-dimensional observations and long horizons, but are known to be hard to
generalize across tasks. Model-based RL, on the other hand, learns
task-agnostic models of the world that naturally enables transfer across
different reward functions, but struggles to scale to complex environments due
to the compounding error. To get the best of both worlds, we propose a
self-supervised reinforcement learning method that enables the transfer of
behaviors across tasks with different rewards, while circumventing the
challenges of model-based RL. In particular, we show self-supervised
pre-training of model-free reinforcement learning with a number of random
features as rewards allows implicit modeling of long-horizon environment
dynamics. Then, planning techniques like model-predictive control using these
implicit models enable fast adaptation to problems with new reward functions.
Our method is self-supervised in that it can be trained on offline datasets
without reward labels, but can then be quickly deployed on new tasks. We
validate that our proposed method enables transfer across tasks on a variety of
manipulation and locomotion domains in simulation, opening the door to
generalist decision-making agents.
|
[
"cs.LG",
"cs.AI"
] | false |
2305.17255
|
2023-05-26T20:54:18Z
|
FineMorphs: Affine-diffeomorphic sequences for regression
|
[
"Michele Lohr",
"Laurent Younes"
] |
A multivariate regression model of affine and diffeomorphic transformation
sequences - FineMorphs - is presented. Leveraging concepts from shape analysis,
model states are optimally "reshaped" by diffeomorphisms generated by smooth
vector fields during learning. Affine transformations and vector fields are
optimized within an optimal control setting, and the model can naturally reduce
(or increase) dimensionality and adapt to large datasets via suboptimal vector
fields. An existence proof of solution and necessary conditions for optimality
for the model are derived. Experimental results on real datasets from the UCI
repository are presented, with favorable results in comparison with
state-of-the-art in the literature and densely-connected neural networks in
TensorFlow.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.17277
|
2023-05-26T21:49:37Z
|
Optimizing NOTEARS Objectives via Topological Swaps
|
[
"Chang Deng",
"Kevin Bello",
"Bryon Aragam",
"Pradeep Ravikumar"
] |
Recently, an intriguing class of non-convex optimization problems has emerged
in the context of learning directed acyclic graphs (DAGs). These problems
involve minimizing a given loss or score function, subject to a non-convex
continuous constraint that penalizes the presence of cycles in a graph. In this
work, we delve into the optimization challenges associated with this class of
non-convex programs. To address these challenges, we propose a bi-level
algorithm that leverages the non-convex constraint in a novel way. The outer
level of the algorithm optimizes over topological orders by iteratively
swapping pairs of nodes within the topological order of a DAG. A key innovation
of our approach is the development of an effective method for generating a set
of candidate swapping pairs for each iteration. At the inner level, given a
topological order, we utilize off-the-shelf solvers that can handle linear
constraints. The key advantage of our proposed algorithm is that it is
guaranteed to find a local minimum or a KKT point under weaker conditions
compared to previous work and finds solutions with lower scores. Extensive
experiments demonstrate that our method outperforms state-of-the-art approaches
in terms of achieving a better score. Additionally, our method can also be used
as a post-processing algorithm to significantly improve the score of other
algorithms. Code implementing the proposed method is available at
https://github.com/duntrain/topo.
|
[
"stat.ML",
"cs.LG"
] | false |
2305.17284
|
2023-05-26T22:11:38Z
|
GC-Flow: A Graph-Based Flow Network for Effective Clustering
|
[
"Tianchun Wang",
"Farzaneh Mirzazadeh",
"Xiang Zhang",
"Jie Chen"
] |
Graph convolutional networks (GCNs) are \emph{discriminative models} that
directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised
classification of graph data. While being effective, as a representation
learning approach, the node representations extracted from a GCN often miss
useful information for effective clustering, because the objectives are
different. In this work, we design normalizing flows that replace GCN layers,
leading to a \emph{generative model} that models both the class conditional
likelihood $p(\mathbf{x}|y)$ and the class prior $p(y)$. The resulting neural
network, GC-Flow, retains the graph convolution operations while being equipped
with a Gaussian mixture representation space. It enjoys two benefits: it not
only maintains the predictive power of GCN, but also produces well-separated
clusters, due to the structuring of the representation space. We demonstrate
these benefits on a variety of benchmark data sets. Moreover, we show that
additional parameterization, such as that on the adjacency matrix used for
graph convolutions, yields additional improvement in clustering.
|
[
"cs.LG",
"stat.ML"
] | false |
2305.18210
|
2023-05-26T13:24:17Z
|
Learning Causal Graphs via Monotone Triangular Transport Maps
|
[
"Sina Akbari",
"Luca Ganassali",
"Negar Kiyavash"
] |
We study the problem of causal structure learning from data using optimal
transport (OT). Specifically, we first provide a constraint-based method which
builds upon lower-triangular monotone parametric transport maps to design
conditional independence tests which are agnostic to the noise distribution. We
provide an algorithm for causal discovery up to Markov Equivalence with no
assumptions on the structural equations/noise distributions, which allows for
settings with latent variables. Our approach also extends to score-based causal
discovery by providing a novel means for defining scores. This allows us to
uniquely recover the causal graph under additional identifiability and
structural assumptions, such as additive noise or post-nonlinear models. We
provide experimental results to compare the proposed approach with the state of
the art on both synthetic and real-world datasets.
|
[
"stat.ME",
"cs.LG"
] | false |
2305.16544
|
2023-05-26T00:03:51Z
|
Inductive detection of Influence Operations via Graph Learning
|
[
"Nicholas A. Gabriel",
"David A. Broniatowski",
"Neil F. Johnson"
] |
Influence operations are large-scale efforts to manipulate public opinion.
The rapid detection and disruption of these operations is critical for healthy
public discourse. Emergent AI technologies may enable novel operations which
evade current detection methods and influence public discourse on social media
with greater scale, reach, and specificity. New methods with inductive learning
capacity will be needed to identify these novel operations before they
indelibly alter public opinion and events. We develop an inductive learning
framework which: 1) determines content- and graph-based indicators that are not
specific to any operation; 2) uses graph learning to encode abstract signatures
of coordinated manipulation; and 3) evaluates generalization capacity by
training and testing models across operations originating from Russia, China,
and Iran. We find that this framework enables strong cross-operation
generalization while also revealing salient
indicators$\unicode{x2013}$illustrating a generic approach which directly
complements transductive methodologies, thereby enhancing detection coverage.
|
[
"cs.LG",
"cs.CR",
"cs.SI",
"physics.soc-ph"
] | false |
2305.16625
|
2023-05-26T04:34:28Z
|
Set-based Neural Network Encoding
|
[
"Bruno Andreis",
"Soro Bedionita",
"Sung Ju Hwang"
] |
We propose an approach to neural network weight encoding for generalization
performance prediction that utilizes set-to-set and set-to-vector functions to
efficiently encode neural network parameters. Our approach is capable of
encoding neural networks in a modelzoo of mixed architecture and different
parameter sizes as opposed to previous approaches that require custom encoding
models for different architectures. Furthermore, our \textbf{S}et-based
\textbf{N}eural network \textbf{E}ncoder (SNE) takes into consideration the
hierarchical computational structure of neural networks by utilizing a
layer-wise encoding scheme that culminates to encoding all layer-wise encodings
to obtain the neural network encoding vector. Additionally, we introduce a
\textit{pad-chunk-encode} pipeline to efficiently encode neural network layers
that is adjustable to computational and memory constraints. We also introduce
two new tasks for neural network generalization performance prediction:
cross-dataset and cross-architecture. In cross-dataset performance prediction,
we evaluate how well performance predictors generalize across modelzoos trained
on different datasets but of the same architecture. In cross-architecture
performance prediction, we evaluate how well generalization performance
predictors transfer to modelzoos of different architecture. Experimentally, we
show that SNE outperforms the relevant baselines on the cross-dataset task and
provide the first set of results on the cross-architecture task.
|
[
"cs.LG",
"cs.AI",
"cs.NE"
] | false |
2305.16699
|
2023-05-26T07:39:26Z
|
Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in
End-to-End Zero-Shot Speech Synthesis
|
[
"Seongyeon Park",
"Bohyung Kim",
"Tae-hyun Oh"
] |
Recently, zero-shot TTS and VC methods have gained attention due to their
practicality of being able to generate voices even unseen during training.
Among these methods, zero-shot modifications of the VITS model have shown
superior performance, while having useful properties inherited from VITS.
However, the performance of VITS and VITS-based zero-shot models vary
dramatically depending on how the losses are balanced. This can be problematic,
as it requires a burdensome procedure of tuning loss balance hyper-parameters
to find the optimal balance. In this work, we propose a novel framework that
finds this optimum without search, by inducing the decoder of VITS-based models
to its full reconstruction ability. With our framework, we show superior
performance compared to baselines in zero-shot TTS and VC, achieving
state-of-the-art performance. Furthermore, we show the robustness of our
framework in various settings. We provide an explanation for the results in the
discussion.
|
[
"eess.AS",
"cs.AI",
"cs.LG"
] | false |
2305.16708
|
2023-05-26T07:53:12Z
|
A Hierarchical Approach to Population Training for Human-AI
Collaboration
|
[
"Yi Loo",
"Chen Gong",
"Malika Meghjani"
] |
A major challenge for deep reinforcement learning (DRL) agents is to
collaborate with novel partners that were not encountered by them during the
training phase. This is specifically worsened by an increased variance in
action responses when the DRL agents collaborate with human partners due to the
lack of consistency in human behaviors. Recent work have shown that training a
single agent as the best response to a diverse population of training partners
significantly increases an agent's robustness to novel partners. We further
enhance the population-based training approach by introducing a Hierarchical
Reinforcement Learning (HRL) based method for Human-AI Collaboration. Our agent
is able to learn multiple best-response policies as its low-level policy while
at the same time, it learns a high-level policy that acts as a manager which
allows the agent to dynamically switch between the low-level best-response
policies based on its current partner. We demonstrate that our method is able
to dynamically adapt to novel partners of different play styles and skill
levels in the 2-player collaborative Overcooked game environment. We also
conducted a human study in the same environment to test the effectiveness of
our method when partnering with real human subjects.
|
[
"cs.AI",
"cs.LG",
"cs.MA"
] | false |
2305.16836
|
2023-05-26T11:26:49Z
|
A Robust Probabilistic Approach to Stochastic Subspace Identification
|
[
"Brandon J. O'Connell",
"Timothy J. Rogers"
] |
Modal parameter estimation of operational structures is often a challenging
task when confronted with unwanted distortions (outliers) in field
measurements. Atypical observations present a problem to operational modal
analysis (OMA) algorithms, such as stochastic subspace identification (SSI),
severely biasing parameter estimates and resulting in misidentification of the
system. Despite this predicament, no simple mechanism currently exists capable
of dealing with such anomalies in SSI. Addressing this problem, this paper
first introduces a novel probabilistic formulation of stochastic subspace
identification (Prob-SSI), realised using probabilistic projections.
Mathematically, the equivalence between this model and the classic algorithm is
demonstrated. This fresh perspective, viewing SSI as a problem in probabilistic
inference, lays the necessary mathematical foundation to enable a plethora of
new, more sophisticated OMA approaches. To this end, a statistically robust SSI
algorithm (robust Prob-SSI) is developed, capable of providing a principled and
automatic way of handling outlying or anomalous data in the measured
timeseries, such as may occur in field recordings, e.g. intermittent sensor
dropout. Robust Prob-SSI is shown to outperform conventional SSI when
confronted with 'corrupted' data, exhibiting improved identification
performance and higher levels of confidence in the found poles when viewing
consistency (stabilisation) diagrams. Similar benefits are also demonstrated on
the Z24 Bridge benchmark dataset, highlighting enhanced performance on measured
systems.
|
[
"stat.ML",
"cs.LG",
"eess.SP"
] | false |
2305.16837
|
2023-05-26T11:29:06Z
|
ChatGPT: A Study on its Utility for Ubiquitous Software Engineering
Tasks
|
[
"Giriprasad Sridhara",
"Ranjani H. G.",
"Sourav Mazumdar"
] |
ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot launched by
OpenAI on November 30, 2022. OpenAI's GPT-3 family of large language models
serve as the foundation for ChatGPT. ChatGPT is fine-tuned with both supervised
and reinforcement learning techniques and has received widespread attention for
its articulate responses across diverse domains of knowledge. In this study, we
explore how ChatGPT can be used to help with common software engineering tasks.
Many of the ubiquitous tasks covering the breadth of software engineering such
as ambiguity resolution in software requirements, method name suggestion, test
case prioritization, code review, log summarization can potentially be
performed using ChatGPT. In this study, we explore fifteen common software
engineering tasks using ChatGPT. We juxtapose and analyze ChatGPT's answers
with the respective state of the art outputs (where available) and/or human
expert ground truth. Our experiments suggest that for many tasks, ChatGPT does
perform credibly and the response from it is detailed and often better than the
human expert output or the state of the art output. However, for a few other
tasks, ChatGPT in its present form provides incorrect answers and hence is not
suited for such tasks.
|
[
"cs.SE",
"cs.AI",
"cs.LG"
] | false |
2305.16845
|
2023-05-26T11:53:53Z
|
An end-to-end strategy for recovering a free-form potential from a
snapshot of stellar coordinates
|
[
"Wassim Tenachi",
"Rodrigo Ibata",
"Foivos I. Diakogiannis"
] |
New large observational surveys such as Gaia are leading us into an era of
data abundance, offering unprecedented opportunities to discover new physical
laws through the power of machine learning. Here we present an end-to-end
strategy for recovering a free-form analytical potential from a mere snapshot
of stellar positions and velocities. First we show how auto-differentiation can
be used to capture an agnostic map of the gravitational potential and its
underlying dark matter distribution in the form of a neural network. However,
in the context of physics, neural networks are both a plague and a blessing as
they are extremely flexible for modeling physical systems but largely consist
in non-interpretable black boxes. Therefore, in addition, we show how a
complementary symbolic regression approach can be used to open up this neural
network into a physically meaningful expression. We demonstrate our strategy by
recovering the potential of a toy isochrone system.
|
[
"astro-ph.GA",
"astro-ph.IM",
"cs.LG",
"physics.comp-ph"
] | false |
2305.16862
|
2023-05-26T12:15:11Z
|
Neural modeling of magnetic tape recorders
|
[
"Otto Mikkonen",
"Alec Wright",
"Eloi Moliner",
"Vesa Välimäki"
] |
The sound of magnetic recording media, such as open-reel and cassette tape
recorders, is still sought after by today's sound practitioners due to the
imperfections embedded in the physics of the magnetic recording process. This
paper proposes a method for digitally emulating this character using neural
networks. The signal chain of the proposed system consists of three main
components: the hysteretic nonlinearity and filtering jointly produced by the
magnetic recording process as well as the record and playback amplifiers, the
fluctuating delay originating from the tape transport, and the combined
additive noise component from various electromagnetic origins. In our approach,
the hysteretic nonlinear block is modeled using a recurrent neural network,
while the delay trajectories and the noise component are generated using
separate diffusion models, which employ U-net deep convolutional neural
networks. According to the conducted objective evaluation, the proposed
architecture faithfully captures the character of the magnetic tape recorder.
The results of this study can be used to construct virtual replicas of vintage
sound recording devices with applications in music production and audio
antiquing tasks.
|
[
"eess.AS",
"cs.LG",
"cs.SD"
] | false |
2305.16903
|
2023-05-26T13:17:20Z
|
Submodular Minimax Optimization: Finding Effective Sets
|
[
"Loay Mualem",
"Ethan R. Elenberg",
"Moran Feldman",
"Amin Karbasi"
] |
Despite the rich existing literature about minimax optimization in continuous
settings, only very partial results of this kind have been obtained for
combinatorial settings. In this paper, we fill this gap by providing a
characterization of submodular minimax optimization, the problem of finding a
set (for either the min or the max player) that is effective against every
possible response. We show when and under what conditions we can find such
sets. We also demonstrate how minimax submodular optimization provides robust
solutions for downstream machine learning applications such as (i) efficient
prompt engineering for question answering, (ii) prompt engineering for dialog
state tracking, (iii) identifying robust waiting locations for ride-sharing,
(iv) ride-share difficulty kernelization, and (v) finding adversarial images.
Our experiments demonstrate that our proposed algorithms consistently
outperform other baselines.
|
[
"cs.LG",
"cs.DM",
"math.OC",
"68R05 (Primary) 90C26, 90C20, 68T20, 68W40 (Secondary)",
"G.2.1; I.2.m; F.2.2"
] | false |
2305.16933
|
2023-05-26T13:48:37Z
|
Representing Piecewise Linear Functions by Functions with Small Arity
|
[
"Christoph Koutschan",
"Bernhard Moser",
"Anton Ponomarchuk",
"Josef Schicho"
] |
A piecewise linear function can be described in different forms: as an
arbitrarily nested expression of $\min$- and $\max$-functions, as a difference
of two convex piecewise linear functions, or as a linear combination of maxima
of affine-linear functions. In this paper, we provide two main results: first,
we show that for every piecewise linear function there exists a linear
combination of $\max$-functions with at most $n+1$ arguments, and give an
algorithm for its computation. Moreover, these arguments are contained in the
finite set of affine-linear functions that coincide with the given function in
some open set. Second, we prove that the piecewise linear function $\max(0,
x_{1}, \ldots, x_{n})$ cannot be represented as a linear combination of maxima
of less than $n+1$ affine-linear arguments. This was conjectured by Wang and
Sun in 2005 in a paper on representations of piecewise linear functions as
linear combination of maxima.
|
[
"cs.SC",
"cs.DM",
"cs.LG",
"math.CO"
] | false |
2305.16974
|
2023-05-26T14:29:33Z
|
Finite Time Regret Bounds for Minimum Variance Control of Autoregressive
Systems with Exogenous Inputs
|
[
"Rahul Singh",
"Akshay Mete",
"Avik Kar",
"P. R. Kumar"
] |
Minimum variance controllers have been employed in a wide-range of industrial
applications. A key challenge experienced by many adaptive controllers is their
poor empirical performance in the initial stages of learning. In this paper, we
address the problem of initializing them so that they provide acceptable
transients, and also provide an accompanying finite-time regret analysis, for
adaptive minimum variance control of an auto-regressive system with exogenous
inputs (ARX). Following [3], we consider a modified version of the Certainty
Equivalence (CE) adaptive controller, which we call PIECE, that utilizes
probing inputs for exploration. We show that it has a $C \log T$ bound on the
regret after $T$ time-steps for bounded noise, and $C\log^2 T$ in the case of
sub-Gaussian noise. The simulation results demonstrate the advantage of PIECE
over the algorithm proposed in [3] as well as the standard Certainty
Equivalence controller especially in the initial learning phase. To the best of
our knowledge, this is the first work that provides finite-time regret bounds
for an adaptive minimum variance controller.
|
[
"eess.SY",
"cs.LG",
"cs.SY"
] | false |
2305.17043
|
2023-05-26T15:52:08Z
|
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing
and Knowledge Discovery
|
[
"Patrick Wagner",
"Temesgen Mehari",
"Wilhelm Haverkamp",
"Nils Strodthoff"
] |
Deep neural networks have become increasingly popular for analyzing ECG data
because of their ability to accurately identify cardiac conditions and hidden
clinical factors. However, the lack of transparency due to the black box nature
of these models is a common concern. To address this issue, explainable AI
(XAI) methods can be employed. In this study, we present a comprehensive
analysis of post-hoc XAI methods, investigating the local (attributions per
sample) and global (based on domain expert concepts) perspectives. We have
established a set of sanity checks to identify sensible attribution methods,
and we provide quantitative evidence in accordance with expert rules. This
dataset-wide analysis goes beyond anecdotal evidence by aggregating data across
patient subgroups. Furthermore, we demonstrate how these XAI techniques can be
utilized for knowledge discovery, such as identifying subtypes of myocardial
infarction. We believe that these proposed methods can serve as building blocks
for a complementary assessment of the internal validity during a certification
process, as well as for knowledge discovery in the field of ECG analysis.
|
[
"eess.SP",
"cs.LG",
"stat.ML"
] | false |
2305.17071
|
2023-05-26T16:28:26Z
|
Adversarial Attacks on Online Learning to Rank with Click Feedback
|
[
"Jinhang Zuo",
"Zhiyao Zhang",
"Zhiyong Wang",
"Shuai Li",
"Mohammad Hajiesmaili",
"Adam Wierman"
] |
Online learning to rank (OLTR) is a sequential decision-making problem where
a learning agent selects an ordered list of items and receives feedback through
user clicks. Although potential attacks against OLTR algorithms may cause
serious losses in real-world applications, little is known about adversarial
attacks on OLTR. This paper studies attack strategies against multiple variants
of OLTR. Our first result provides an attack strategy against the UCB algorithm
on classical stochastic bandits with binary feedback, which solves the key
issues caused by bounded and discrete feedback that previous works can not
handle. Building on this result, we design attack algorithms against UCB-based
OLTR algorithms in position-based and cascade models. Finally, we propose a
general attack strategy against any algorithm under the general click model.
Each attack algorithm manipulates the learning agent into choosing the target
attack item $T-o(T)$ times, incurring a cumulative cost of $o(T)$. Experiments
on synthetic and real data further validate the effectiveness of our proposed
attack algorithms.
|
[
"cs.LG",
"cs.CR",
"cs.IR"
] | false |
2305.17299
|
2023-05-26T23:00:19Z
|
Improving Stability in Decision Tree Models
|
[
"Dimitris Bertsimas",
"Vassilis Digalakis Jr"
] |
Owing to their inherently interpretable structure, decision trees are
commonly used in applications where interpretability is essential. Recent work
has focused on improving various aspects of decision trees, including their
predictive power and robustness; however, their instability, albeit
well-documented, has been addressed to a lesser extent. In this paper, we take
a step towards the stabilization of decision tree models through the lens of
real-world health care applications due to the relevance of stability and
interpretability in this space. We introduce a new distance metric for decision
trees and use it to determine a tree's level of stability. We propose a novel
methodology to train stable decision trees and investigate the existence of
trade-offs that are inherent to decision tree models - including between
stability, predictive power, and interpretability. We demonstrate the value of
the proposed methodology through an extensive quantitative and qualitative
analysis of six case studies from real-world health care applications, and we
show that, on average, with a small 4.6% decrease in predictive power, we gain
a significant 38% improvement in the model's stability.
|
[
"stat.ML",
"cs.AI",
"cs.LG",
"math.OC"
] | false |
2305.17300
|
2023-05-26T23:04:53Z
|
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained
Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
|
[
"Erik C. Johnson",
"Brian S. Robinson",
"Gautam K. Vallabha",
"Justin Joyce",
"Jordan K. Matelsky",
"Raphael Norman-Tenazas",
"Isaac Western",
"Marisel Villafañe-Delgado",
"Martha Cervantes",
"Michael S. Robinette",
"Arun V. Reddy",
"Lindsey Kitchell",
"Patricia K. Rivlin",
"Elizabeth P. Reilly",
"Nathan Drenkow",
"Matthew J. Roos",
"I-Jeng Wang",
"Brock A. Wester",
"William R. Gray-Roncal",
"Joan A. Hoffmann"
] |
Despite the progress in deep learning networks, efficient learning at the
edge (enabling adaptable, low-complexity machine learning solutions) remains a
critical need for defense and commercial applications. We envision a pipeline
to utilize large neuroimaging datasets, including maps of the brain which
capture neuron and synapse connectivity, to improve machine learning
approaches. We have pursued different approaches within this pipeline
structure. First, as a demonstration of data-driven discovery, the team has
developed a technique for discovery of repeated subcircuits, or motifs. These
were incorporated into a neural architecture search approach to evolve network
architectures. Second, we have conducted analysis of the heading direction
circuit in the fruit fly, which performs fusion of visual and angular velocity
features, to explore augmenting existing computational models with new insight.
Our team discovered a novel pattern of connectivity, implemented a new model,
and demonstrated sensor fusion on a robotic platform. Third, the team analyzed
circuitry for memory formation in the fruit fly connectome, enabling the design
of a novel generative replay approach. Finally, the team has begun analysis of
connectivity in mammalian cortex to explore potential improvements to
transformer networks. These constraints increased network robustness on the
most challenging examples in the CIFAR-10-C computer vision robustness
benchmark task, while reducing learnable attention parameters by over an order
of magnitude. Taken together, these results demonstrate multiple potential
approaches to utilize insight from neural systems for developing robust and
efficient machine learning techniques.
|
[
"cs.NE",
"cs.AI",
"cs.LG"
] | false |
2305.18205
|
2023-05-26T13:24:33Z
|
Pulse shape discrimination based on the Tempotron: a powerful classifier
on GPU
|
[
"Haoran Liu",
"Peng Li",
"Ming-Zhe Liu",
"Kai-Ming Wang",
"Zhuo Zuo",
"Bing-Qi Liu"
] |
This study introduces the Tempotron, a powerful classifier based on a
third-generation neural network model, for pulse shape discrimination. By
eliminating the need for manual feature extraction, the Tempotron model can
process pulse signals directly, generating discrimination results based on
learned prior knowledge. The study performed experiments using GPU
acceleration, resulting in over a 500 times speedup compared to the CPU-based
model, and investigated the impact of noise augmentation on the Tempotron's
performance. Experimental results showed that the Tempotron is a potent
classifier capable of achieving high discrimination accuracy. Furthermore,
analyzing the neural activity of Tempotron during training shed light on its
learning characteristics and aided in selecting the Tempotron's
hyperparameters. The dataset used in this study and the source code of the
GPU-based Tempotron are publicly available on GitHub at
https://github.com/HaoranLiu507/TempotronGPU.
|
[
"eess.SP",
"cs.LG",
"nucl-ex"
] | false |
2305.18356
|
2023-05-26T17:40:25Z
|
RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor
Search
|
[
"Vani Nagarajan",
"Durga Mandarapu",
"Milind Kulkarni"
] |
The problem of identifying the k-Nearest Neighbors (kNNS) of a point has
proven to be very useful both as a standalone application and as a subroutine
in larger applications. Given its far-reaching applicability in areas such as
machine learning and point clouds, extensive research has gone into leveraging
GPU acceleration to solve this problem. Recent work has shown that using Ray
Tracing cores in recent GPUs to accelerate kNNS is much more efficient compared
to traditional acceleration using shader cores. However, the existing
translation of kNNS to a ray tracing problem imposes a constraint on the search
space for neighbors. Due to this, we can only use RT cores to accelerate
fixed-radius kNNS, which requires the user to set a search radius a priori and
hence can miss neighbors. In this work, we propose TrueKNN, the first unbounded
RT-accelerated neighbor search. TrueKNN adopts an iterative approach where we
incrementally grow the search space until all points have found their k
neighbors. We show that our approach is orders of magnitude faster than
existing approaches and can even be used to accelerate fixed-radius neighbor
searches.
|
[
"cs.LG",
"cs.CG",
"cs.PF"
] | false |
2306.06109
|
2023-05-26T04:13:31Z
|
Learning to Quantize Vulnerability Patterns and Match to Locate
Statement-Level Vulnerabilities
|
[
"Michael Fu",
"Trung Le",
"Van Nguyen",
"Chakkrit Tantithamthavorn",
"Dinh Phung"
] |
Deep learning (DL) models have become increasingly popular in identifying
software vulnerabilities. Prior studies found that vulnerabilities across
different vulnerable programs may exhibit similar vulnerable scopes, implicitly
forming discernible vulnerability patterns that can be learned by DL models
through supervised training. However, vulnerable scopes still manifest in
various spatial locations and formats within a program, posing challenges for
models to accurately identify vulnerable statements. Despite this challenge,
state-of-the-art vulnerability detection approaches fail to exploit the
vulnerability patterns that arise in vulnerable programs. To take full
advantage of vulnerability patterns and unleash the ability of DL models, we
propose a novel vulnerability-matching approach in this paper, drawing
inspiration from program analysis tools that locate vulnerabilities based on
pre-defined patterns. Specifically, a vulnerability codebook is learned, which
consists of quantized vectors representing various vulnerability patterns.
During inference, the codebook is iterated to match all learned patterns and
predict the presence of potential vulnerabilities within a given program. Our
approach was extensively evaluated on a real-world dataset comprising more than
188,000 C/C++ functions. The evaluation results show that our approach achieves
an F1-score of 94% (6% higher than the previous best) and 82% (19% higher than
the previous best) for function and statement-level vulnerability
identification, respectively. These substantial enhancements highlight the
effectiveness of our approach to identifying vulnerabilities. The training code
and pre-trained models are available at https://github.com/optimatch/optimatch.
|
[
"cs.CR",
"cs.AI",
"cs.LG"
] | false |
2307.06821
|
2023-05-26T17:02:29Z
|
Equalization in Dispersion-Managed Systems Using Learned Digital
Back-Propagation
|
[
"Mohannad Abu-Romoh",
"Nelson Costa",
"Yves Jaouën",
"Antonio Napoli",
"João Pedro",
"Bernhard Spinnler",
"Mansoor Yousefi"
] |
In this paper, we investigate the use of the learned digital back-propagation
(LDBP) for equalizing dual-polarization fiber-optic transmission in
dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the
parameters of DBP using the stochastic gradient descent. We evaluate DBP and
LDBP in a simulated WDM dual-polarization fiber transmission system operating
at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a
2016 km link with 15% residual dispersion. Our results show that in
single-channel transmission, LDBP achieves an effective signal-to-noise ratio
improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and
DBP. In WDM transmission, the corresponding $Q$-factor gains are 1.1 dB and 0.4
dB, respectively. Additionally, we conduct a complexity analysis, which reveals
that a frequency-domain implementation of LDBP and DBP is more favorable in
terms of complexity than the time-domain implementation. These findings
demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM
fiber-optic transmission systems.
|
[
"cs.NI",
"cs.LG",
"eess.SP"
] | false |
2305.16620
|
2023-05-26T04:27:48Z
|
Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing
Uncertainty
|
[
"Anshul Nayak",
"Azim Eskandarian",
"Zachary Doerzaph",
"Prasenjit Ghorai"
] |
One of the fundamental challenges in the prediction of dynamic agents is
robustness. Usually, most predictions are deterministic estimates of future
states which are over-confident and prone to error. Recently, few works have
addressed capturing uncertainty during forecasting of future states. However,
these probabilistic estimation methods fail to account for the upstream noise
in perception data during tracking. Sensors always have noise and state
estimation becomes even more difficult under adverse weather conditions and
occlusion. Traditionally, Bayes filters have been used to fuse information from
noisy sensors to update states with associated belief. But, they fail to
address non-linearities and long-term predictions. Therefore, we propose an
end-to-end estimator that can take noisy sensor measurements and make robust
future state predictions with uncertainty bounds while simultaneously taking
into consideration the upstream perceptual uncertainty. For the current
research, we consider an encoder-decoder based deep ensemble network for
capturing both perception and predictive uncertainty simultaneously. We
compared the current model to other approximate Bayesian inference methods.
Overall, deep ensembles provided more robust predictions and the consideration
of upstream uncertainty further increased the estimation accuracy for the
model.
|
[
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | false |
2305.16892
|
2023-05-26T12:53:13Z
|
Feature Adaptation for Sparse Linear Regression
|
[
"Jonathan Kelner",
"Frederic Koehler",
"Raghu Meka",
"Dhruv Rohatgi"
] |
Sparse linear regression is a central problem in high-dimensional statistics.
We study the correlated random design setting, where the covariates are drawn
from a multivariate Gaussian $N(0,\Sigma)$, and we seek an estimator with small
excess risk.
If the true signal is $t$-sparse, information-theoretically, it is possible
to achieve strong recovery guarantees with only $O(t\log n)$ samples. However,
computationally efficient algorithms have sample complexity linear in (some
variant of) the condition number of $\Sigma$. Classical algorithms such as the
Lasso can require significantly more samples than necessary even if there is
only a single sparse approximate dependency among the covariates.
We provide a polynomial-time algorithm that, given $\Sigma$, automatically
adapts the Lasso to tolerate a small number of approximate dependencies. In
particular, we achieve near-optimal sample complexity for constant sparsity and
if $\Sigma$ has few ``outlier'' eigenvalues. Our algorithm fits into a broader
framework of feature adaptation for sparse linear regression with
ill-conditioned covariates. With this framework, we additionally provide the
first polynomial-factor improvement over brute-force search for constant
sparsity $t$ and arbitrary covariance $\Sigma$.
|
[
"cs.DS",
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | false |
2305.17052
|
2023-05-26T16:00:59Z
|
A Framework for Incentivized Collaborative Learning
|
[
"Xinran Wang",
"Qi Le",
"Ahmad Faraz Khan",
"Jie Ding",
"Ali Anwar"
] |
Collaborations among various entities, such as companies, research labs, AI
agents, and edge devices, have become increasingly crucial for achieving
machine learning tasks that cannot be accomplished by a single entity alone.
This is likely due to factors such as security constraints, privacy concerns,
and limitations in computation resources. As a result, collaborative learning
(CL) research has been gaining momentum. However, a significant challenge in
practical applications of CL is how to effectively incentivize multiple
entities to collaborate before any collaboration occurs. In this study, we
propose ICL, a general framework for incentivized collaborative learning, and
provide insights into the critical issue of when and why incentives can improve
collaboration performance. Furthermore, we show the broad applicability of ICL
to specific cases in federated learning, assisted learning, and multi-armed
bandit with both theory and experimental results.
|
[
"cs.LG",
"cs.AI",
"cs.CY",
"cs.GT",
"cs.MA"
] | false |
2305.19279
|
2023-05-26T08:17:22Z
|
Data-Driven Games in Computational Mechanics
|
[
"Kerstin Weinberg",
"Laurent Strainier",
"Sergio Conti",
"Michael Ortiz"
] |
We resort to game theory in order to formulate Data-Driven methods for solid
mechanics in which stress and strain players pursue different objectives. The
objective of the stress player is to minimize the discrepancy to a material
data set, whereas the objective of the strain player is to ensure the
admissibility of the mechanical state, in the sense of compatibility and
equilibrium. We show that, unlike the cooperative Data-Driven games proposed in
the past, the new non-cooperative Data-Driven games identify an effective
material law from the data and reduce to conventional displacement
boundary-value problems, which facilitates their practical implementation.
However, unlike supervised machine learning methods, the proposed
non-cooperative Data-Driven games are unsupervised, ansatz-free and
parameter-free. In particular, the effective material law is learned from the
data directly, without recourse to regression to a parameterized class of
functions such as neural networks. We present analysis that elucidates
sufficient conditions for convergence of the Data-Driven solutions with respect
to the data. We also present selected examples of implementation and
application that demonstrate the range and versatility of the approach.
|
[
"cs.CE",
"cond-mat.mtrl-sci",
"cs.LG",
"cs.NA",
"math.NA"
] | false |
2305.16748
|
2023-05-26T08:50:49Z
|
A Decentralized Spike-based Learning Framework for Sequential Capture in
Discrete Perimeter Defense Problem
|
[
"Mohammed Thousif",
"Shridhar Velhal",
"Suresh Sundaram",
"Shirin Dora"
] |
This paper proposes a novel Decentralized Spike-based Learning (DSL)
framework for the discrete Perimeter Defense Problem (d-PDP). A team of
defenders is operating on the perimeter to protect the circular territory from
radially incoming intruders. At first, the d-PDP is formulated as a
spatio-temporal multi-task assignment problem (STMTA). The problem of STMTA is
then converted into a multi-label learning problem to obtain labels of segments
that defenders have to visit in order to protect the perimeter. The DSL
framework uses a Multi-Label Classifier using Synaptic Efficacy Function
spiking neuRON (MLC-SEFRON) network for deterministic multi-label learning.
Each defender contains a single MLC-SEFRON network. Each MLC-SEFRON network is
trained independently using input from its own perspective for decentralized
operations. The input spikes to the MLC-SEFRON network can be directly obtained
from the spatio-temporal information of defenders and intruders without any
extra pre-processing step. The output of MLC-SEFRON contains the labels of
segments that a defender has to visit in order to protect the perimeter. Based
on the multi-label output from the MLC-SEFRON a trajectory is generated for a
defender using a Consensus-Based Bundle Algorithm (CBBA) in order to capture
the intruders. The target multi-label output for training MLC-SEFRON is
obtained from an expert policy. Also, the MLC-SEFRON trained for a defender can
be directly used for obtaining labels of segments assigned to another defender
without any retraining. The performance of MLC-SEFRON has been evaluated for
full observation and partial observation scenarios of the defender. The overall
performance of the DSL framework is then compared with expert policy along with
other existing learning algorithms. The scalability of the DSL has been
evaluated using an increasing number of defenders.
|
[
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA",
"cs.NE",
"cs.SY",
"eess.SY"
] | false |
2305.17313
|
2023-05-27T00:17:19Z
|
Super-Resolution of License Plate Images Using Attention Modules and
Sub-Pixel Convolution Layers
|
[
"Valfride Nascimento",
"Rayson Laroca",
"Jorge de A. Lambert",
"William Robson Schwartz",
"David Menotti"
] |
Recent years have seen significant developments in the field of License Plate
Recognition (LPR) through the integration of deep learning techniques and the
increasing availability of training data. Nevertheless, reconstructing license
plates (LPs) from low-resolution (LR) surveillance footage remains challenging.
To address this issue, we introduce a Single-Image Super-Resolution (SISR)
approach that integrates attention and transformer modules to enhance the
detection of structural and textural features in LR images. Our approach
incorporates sub-pixel convolution layers (also known as PixelShuffle) and a
loss function that uses an Optical Character Recognition (OCR) model for
feature extraction. We trained the proposed architecture on synthetic images
created by applying heavy Gaussian noise to high-resolution LP images from two
public datasets, followed by bicubic downsampling. As a result, the generated
images have a Structural Similarity Index Measure (SSIM) of less than 0.10. Our
results show that our approach for reconstructing these low-resolution
synthesized images outperforms existing ones in both quantitative and
qualitative measures. Our code is publicly available at
https://github.com/valfride/lpr-rsr-ext/
|
[
"cs.CV"
] | false |
2305.17374
|
2023-05-27T05:37:02Z
|
LE2Fusion: A novel local edge enhancement module for infrared and
visible image fusion
|
[
"Yongbiao Xiao",
"Hui Li",
"Chunyang Cheng",
"Xiaoning Song"
] |
Infrared and visible image fusion task aims to generate a fused image which
contains salient features and rich texture details from multi-source images.
However, under complex illumination conditions, few algorithms pay attention to
the edge information of local regions which is crucial for downstream tasks. To
this end, we propose a fusion network based on the local edge enhancement,
named LE2Fusion. Specifically, a local edge enhancement (LE2) module is
proposed to improve the edge information under complex illumination conditions
and preserve the essential features of image. For feature extraction, a
multi-scale residual attention (MRA) module is applied to extract rich
features. Then, with LE2, a set of enhancement weights are generated which are
utilized in feature fusion strategy and used to guide the image reconstruction.
To better preserve the local detail information and structure information, the
pixel intensity loss function based on the local region is also presented. The
experiments demonstrate that the proposed method exhibits better fusion
performance than the state-of-the-art fusion methods on public datasets.
|
[
"cs.CV"
] | false |
2305.17451
|
2023-05-27T11:30:32Z
|
Analysis over vision-based models for pedestrian action anticipation
|
[
"Lina Achaji",
"Julien Moreau",
"François Aioun",
"François Charpillet"
] |
Anticipating human actions in front of autonomous vehicles is a challenging
task. Several papers have recently proposed model architectures to address this
problem by combining multiple input features to predict pedestrian crossing
actions. This paper focuses specifically on using images of the pedestrian's
context as an input feature. We present several spatio-temporal model
architectures that utilize standard CNN and Transformer modules to serve as a
backbone for pedestrian anticipation. However, the objective of this paper is
not to surpass state-of-the-art benchmarks but rather to analyze the positive
and negative predictions of these models. Therefore, we provide insights on the
explainability of vision-based Transformer models in the context of pedestrian
action prediction. We will highlight cases where the model can achieve correct
quantitative results but falls short in providing human-like explanations
qualitatively, emphasizing the importance of investing in explainability for
pedestrian action anticipation problems.
|
[
"cs.CV"
] | false |
2305.17463
|
2023-05-27T12:41:23Z
|
Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature
for Local Feature Matching-Based Homography Estimation
|
[
"Yueh-Cheng Huang",
"Chen-Tao Hsu",
"Jen-Hui Chuang"
] |
In computer vision, finding correct point correspondence among images plays
an important role in many applications, such as image stitching, image
retrieval, visual localization, etc. Most of the research works focus on the
matching of local feature before a sampling method is employed, such as RANSAC,
to verify initial matching results via repeated fitting of certain global
transformation among the images. However, incorrect matches may still exist.
Thus, a novel sampling scheme, Pentagon-Match (PMatch), is proposed in this
work to verify the correctness of initially matched keypoints using pentagons
randomly sampled from them. By ensuring shape and location of these pentagons
are view-invariant with various evaluations of cross-ratio (CR), incorrect
matches of keypoint can be identified easily with homography estimated from
correctly matched pentagons. Experimental results show that highly accurate
estimation of homography can be obtained efficiently for planar scenes of the
HPatches dataset, based on keypoint matching results provided by LoFTR.
Besides, accurate outlier identification for the above matching results and
possible extension of the approach for multi-plane situation are also
demonstrated.
|
[
"cs.CV"
] | false |
2305.17522
|
2023-05-27T16:37:41Z
|
Deep Learning based Fingerprint Presentation Attack Detection: A
Comprehensive Survey
|
[
"Hailin Li",
"Raghavendra Ramachandra"
] |
The vulnerabilities of fingerprint authentication systems have raised
security concerns when adapting them to highly secure access-control
applications. Therefore, Fingerprint Presentation Attack Detection (FPAD)
methods are essential for ensuring reliable fingerprint authentication. Owing
to the lack of generation capacity of traditional handcrafted based approaches,
deep learning-based FPAD has become mainstream and has achieved remarkable
performance in the past decade. Existing reviews have focused more on
hand-cratfed rather than deep learning-based methods, which are outdated. To
stimulate future research, we will concentrate only on recent
deep-learning-based FPAD methods. In this paper, we first briefly introduce the
most common Presentation Attack Instruments (PAIs) and publicly available
fingerprint Presentation Attack (PA) datasets. We then describe the existing
deep-learning FPAD by categorizing them into contact, contactless, and
smartphone-based approaches. Finally, we conclude the paper by discussing the
open challenges at the current stage and emphasizing the potential future
perspective.
|
[
"cs.CV"
] | false |
2305.17569
|
2023-05-27T20:12:19Z
|
Collaborative Multi-Agent Video Fast-Forwarding
|
[
"Shuyue Lan",
"Zhilu Wang",
"Ermin Wei",
"Amit K. Roy-Chowdhury",
"Qi Zhu"
] |
Multi-agent applications have recently gained significant popularity. In many
computer vision tasks, a network of agents, such as a team of robots with
cameras, could work collaboratively to perceive the environment for efficient
and accurate situation awareness. However, these agents often have limited
computation, communication, and storage resources. Thus, reducing resource
consumption while still providing an accurate perception of the environment
becomes an important goal when deploying multi-agent systems. To achieve this
goal, we identify and leverage the overlap among different camera views in
multi-agent systems for reducing the processing, transmission and storage of
redundant/unimportant video frames. Specifically, we have developed two
collaborative multi-agent video fast-forwarding frameworks in distributed and
centralized settings, respectively. In these frameworks, each individual agent
can selectively process or skip video frames at adjustable paces based on
multiple strategies via reinforcement learning. Multiple agents then
collaboratively sense the environment via either 1) a consensus-based
distributed framework called DMVF that periodically updates the fast-forwarding
strategies of agents by establishing communication and consensus among
connected neighbors, or 2) a centralized framework called MFFNet that utilizes
a central controller to decide the fast-forwarding strategies for agents based
on collected data. We demonstrate the efficacy and efficiency of our proposed
frameworks on a real-world surveillance video dataset VideoWeb and a new
simulated driving dataset CarlaSim, through extensive simulations and
deployment on an embedded platform with TCP communication. We show that
compared with other approaches in the literature, our frameworks achieve better
coverage of important frames, while significantly reducing the number of frames
processed at each agent.
|
[
"cs.CV"
] | false |
2305.17338
|
2023-05-27T02:38:54Z
|
Multi-label Video Classification for Underwater Ship Inspection
|
[
"Md Abulkalam Azad",
"Ahmed Mohammed",
"Maryna Waszak",
"Brian Elvesæter",
"Martin Ludvigsen"
] |
Today ship hull inspection including the examination of the external coating,
detection of defects, and other types of external degradation such as corrosion
and marine growth is conducted underwater by means of Remotely Operated
Vehicles (ROVs). The inspection process consists of a manual video analysis
which is a time-consuming and labor-intensive process. To address this, we
propose an automatic video analysis system using deep learning and computer
vision to improve upon existing methods that only consider spatial information
on individual frames in underwater ship hull video inspection. By exploring the
benefits of adding temporal information and analyzing frame-based classifiers,
we propose a multi-label video classification model that exploits the
self-attention mechanism of transformers to capture spatiotemporal attention in
consecutive video frames. Our proposed method has demonstrated promising
results and can serve as a benchmark for future research and development in
underwater video inspection applications.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17355
|
2023-05-27T03:37:33Z
|
Rethinking PRL: A Multiscale Progressively Residual Learning Network for
Inverse Halftoning
|
[
"Feiyu Li",
"Jun Yang"
] |
Image inverse halftoning is a classic image restoration task, aiming to
recover continuous-tone images from halftone images with only bilevel pixels.
Because the halftone images lose much of the original image content, inverse
halftoning is a classic ill-problem. Although existing inverse halftoning
algorithms achieve good performance, their results lose image details and
features. Therefore, it is still a challenge to recover high-quality
continuous-tone images. In this paper, we propose an end-to-end multiscale
progressively residual learning network (MSPRL), which has a UNet architecture
and takes multiscale input images. To make full use of different input image
information, we design a shallow feature extraction module to capture similar
features between images of different scales. We systematically study the
performance of different methods and compare them with our proposed method. In
addition, we employ different training strategies to optimize the model, which
is important for optimizing the training process and improving performance.
Extensive experiments demonstrate that our MSPRL model obtains considerable
performance gains in detail restoration.
|
[
"cs.CV",
"eess.IV"
] | false |
2305.17368
|
2023-05-27T04:55:13Z
|
Instance-based Max-margin for Practical Few-shot Recognition
|
[
"Minghao Fu",
"Ke Zhu",
"Jianxin Wu"
] |
In order to mimic the human few-shot learning (FSL) ability better and to
make FSL closer to real-world applications, this paper proposes a practical FSL
(pFSL) setting. pFSL is based on unsupervised pretrained models (analogous to
human prior knowledge) and recognizes many novel classes simultaneously.
Compared to traditional FSL, pFSL is simpler in its formulation, easier to
evaluate, more challenging and more practical. To cope with the rarity of
training examples, this paper proposes IbM2, an instance-based max-margin
method not only for the new pFSL setting, but also works well in traditional
FSL scenarios. Based on the Gaussian Annulus Theorem, IbM2 converts random
noise applied to the instances into a mechanism to achieve maximum margin in
the many-way pFSL (or traditional FSL) recognition task. Experiments with
various self-supervised pretraining methods and diverse many- or few-way FSL
tasks show that IbM2 almost always leads to improvements compared to its
respective baseline methods, and in most cases the improvements are
significant. With both the new pFSL setting and novel IbM2 method, this paper
shows that practical few-shot learning is both viable and promising.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17388
|
2023-05-27T06:46:42Z
|
MPCHAT: Towards Multimodal Persona-Grounded Conversation
|
[
"Jaewoo Ahn",
"Yeda Song",
"Sangdoo Yun",
"Gunhee Kim"
] |
In order to build self-consistent personalized dialogue agents, previous
research has mostly focused on textual persona that delivers personal facts or
personalities. However, to fully describe the multi-faceted nature of persona,
image modality can help better reveal the speaker's personal characteristics
and experiences in episodic memory (Rubin et al., 2003; Conway, 2009). In this
work, we extend persona-based dialogue to the multimodal domain and make two
main contributions. First, we present the first multimodal persona-based
dialogue dataset named MPCHAT, which extends persona with both text and images
to contain episodic memories. Second, we empirically show that incorporating
multimodal persona, as measured by three proposed multimodal persona-grounded
dialogue tasks (i.e., next response prediction, grounding persona prediction,
and speaker identification), leads to statistically significant performance
improvements across all tasks. Thus, our work highlights that multimodal
persona is crucial for improving multimodal dialogue comprehension, and our
MPCHAT serves as a high-quality resource for this research.
|
[
"cs.CL",
"cs.CV"
] | false |
2305.17398
|
2023-05-27T07:40:07Z
|
NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from
Multiview Images
|
[
"Yuan Liu",
"Peng Wang",
"Cheng Lin",
"Xiaoxiao Long",
"Jiepeng Wang",
"Lingjie Liu",
"Taku Komura",
"Wenping Wang"
] |
We present a neural rendering-based method called NeRO for reconstructing the
geometry and the BRDF of reflective objects from multiview images captured in
an unknown environment. Multiview reconstruction of reflective objects is
extremely challenging because specular reflections are view-dependent and thus
violate the multiview consistency, which is the cornerstone for most multiview
reconstruction methods. Recent neural rendering techniques can model the
interaction between environment lights and the object surfaces to fit the
view-dependent reflections, thus making it possible to reconstruct reflective
objects from multiview images. However, accurately modeling environment lights
in the neural rendering is intractable, especially when the geometry is
unknown. Most existing neural rendering methods, which can model environment
lights, only consider direct lights and rely on object masks to reconstruct
objects with weak specular reflections. Therefore, these methods fail to
reconstruct reflective objects, especially when the object mask is not
available and the object is illuminated by indirect lights. We propose a
two-step approach to tackle this problem. First, by applying the split-sum
approximation and the integrated directional encoding to approximate the
shading effects of both direct and indirect lights, we are able to accurately
reconstruct the geometry of reflective objects without any object masks. Then,
with the object geometry fixed, we use more accurate sampling to recover the
environment lights and the BRDF of the object. Extensive experiments
demonstrate that our method is capable of accurately reconstructing the
geometry and the BRDF of reflective objects from only posed RGB images without
knowing the environment lights and the object masks. Codes and datasets are
available at https://github.com/liuyuan-pal/NeRO.
|
[
"cs.CV",
"cs.GR"
] | false |
2305.17431
|
2023-05-27T10:03:36Z
|
Towards Consistent Video Editing with Text-to-Image Diffusion Models
|
[
"Zicheng Zhang",
"Bonan Li",
"Xuecheng Nie",
"Congying Han",
"Tiande Guo",
"Luoqi Liu"
] |
Existing works have advanced Text-to-Image (TTI) diffusion models for video
editing in a one-shot learning manner. Despite their low requirements of data
and computation, these methods might produce results of unsatisfied consistency
with text prompt as well as temporal sequence, limiting their applications in
the real world. In this paper, we propose to address the above issues with a
novel EI$^2$ model towards \textbf{E}nhancing v\textbf{I}deo \textbf{E}diting
cons\textbf{I}stency of TTI-based frameworks. Specifically, we analyze and find
that the inconsistent problem is caused by newly added modules into TTI models
for learning temporal information. These modules lead to covariate shift in the
feature space, which harms the editing capability. Thus, we design EI$^2$ to
tackle the above drawbacks with two classical modules: Shift-restricted
Temporal Attention Module (STAM) and Fine-coarse Frame Attention Module (FFAM).
First, through theoretical analysis, we demonstrate that covariate shift is
highly related to Layer Normalization, thus STAM employs a \textit{Instance
Centering} layer replacing it to preserve the distribution of temporal
features. In addition, {STAM} employs an attention layer with normalized
mapping to transform temporal features while constraining the variance shift.
As the second part, we incorporate {STAM} with a novel {FFAM}, which
efficiently leverages fine-coarse spatial information of overall frames to
further enhance temporal consistency. Extensive experiments demonstrate the
superiority of the proposed EI$^2$ model for text-driven video editing.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17433
|
2023-05-27T10:06:03Z
|
A Unified Framework for Slot based Response Generation in a Multimodal
Dialogue System
|
[
"Mauajama Firdaus",
"Avinash Madasu",
"Asif Ekbal"
] |
Natural Language Understanding (NLU) and Natural Language Generation (NLG)
are the two critical components of every conversational system that handles the
task of understanding the user by capturing the necessary information in the
form of slots and generating an appropriate response in accordance with the
extracted information. Recently, dialogue systems integrated with complementary
information such as images, audio, or video have gained immense popularity. In
this work, we propose an end-to-end framework with the capability to extract
necessary slot values from the utterance and generate a coherent response,
thereby assisting the user to achieve their desired goals in a multimodal
dialogue system having both textual and visual information. The task of
extracting the necessary information is dependent not only on the text but also
on the visual cues present in the dialogue. Similarly, for the generation, the
previous dialog context comprising multimodal information is significant for
providing coherent and informative responses. We employ a multimodal
hierarchical encoder using pre-trained DialoGPT and also exploit the knowledge
base (Kb) to provide a stronger context for both the tasks. Finally, we design
a slot attention mechanism to focus on the necessary information in a given
utterance. Lastly, a decoder generates the corresponding response for the given
dialogue context and the extracted slot values. Experimental results on the
Multimodal Dialogue Dataset (MMD) show that the proposed framework outperforms
the baselines approaches in both the tasks. The code is available at
https://github.com/avinashsai/slot-gpt.
|
[
"cs.CV",
"cs.CL"
] | false |
2305.17477
|
2023-05-27T13:47:25Z
|
BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
|
[
"Nikita Alutis",
"Egor Chistov",
"Mikhail Dremin",
"Dmitriy Vatolin"
] |
This paper discusses the challenges of evaluating deblurring-methods quality
and proposes a reduced-reference metric based on machine learning. Traditional
quality-assessment metrics such as PSNR and SSIM are common for this task, but
not only do they correlate poorly with subjective assessments, they also
require ground-truth (GT) frames, which can be difficult to obtain in the case
of deblurring. To develop and evaluate our metric, we created a new motion-blur
dataset using a beam splitter. The setup captured various motion types using a
static camera, as most scenes in existing datasets include blur due to camera
motion. We also conducted two large subjective comparisons to aid in metric
development. Our resulting metric requires no GT frames, and it correlates well
with subjective human perception of blur.
|
[
"cs.CV",
"cs.MM"
] | false |
2305.17520
|
2023-05-27T16:33:43Z
|
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense
Active Learning for Super-resolution
|
[
"Vikrant Rangnekar",
"Uddeshya Upadhyay",
"Zeynep Akata",
"Biplab Banerjee"
] |
Dense regression is a widely used approach in computer vision for tasks such
as image super-resolution, enhancement, depth estimation, etc. However, the
high cost of annotation and labeling makes it challenging to achieve accurate
results. We propose incorporating active learning into dense regression models
to address this problem. Active learning allows models to select the most
informative samples for labeling, reducing the overall annotation cost while
improving performance. Despite its potential, active learning has not been
widely explored in high-dimensional computer vision regression tasks like
super-resolution. We address this research gap and propose a new framework
called USIM-DAL that leverages the statistical properties of colour images to
learn informative priors using probabilistic deep neural networks that model
the heteroscedastic predictive distribution allowing uncertainty
quantification. Moreover, the aleatoric uncertainty from the network serves as
a proxy for error that is used for active learning. Our experiments on a wide
variety of datasets spanning applications in natural images (visual genome,
BSD100), medical imaging (histopathology slides), and remote sensing (satellite
images) demonstrate the efficacy of the newly proposed USIM-DAL and superiority
over several dense regression active learning methods.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17559
|
2023-05-27T19:22:25Z
|
Pruning at Initialization -- A Sketching Perspective
|
[
"Noga Bar",
"Raja Giryes"
] |
The lottery ticket hypothesis (LTH) has increased attention to pruning neural
networks at initialization. We study this problem in the linear setting. We
show that finding a sparse mask at initialization is equivalent to the
sketching problem introduced for efficient matrix multiplication. This gives us
tools to analyze the LTH problem and gain insights into it. Specifically, using
the mask found at initialization, we bound the approximation error of the
pruned linear model at the end of training. We theoretically justify previous
empirical evidence that the search for sparse networks may be data independent.
By using the sketching perspective, we suggest a generic improvement to
existing algorithms for pruning at initialization, which we show to be
beneficial in the data-independent case.
|
[
"cs.LG",
"cs.CV"
] | false |
2305.17565
|
2023-05-27T19:58:11Z
|
Self-Supervised Learning of Action Affordances as Interaction Modes
|
[
"Liquan Wang",
"Nikita Dvornik",
"Rafael Dubeau",
"Mayank Mittal",
"Animesh Garg"
] |
When humans perform a task with an articulated object, they interact with the
object only in a handful of ways, while the space of all possible interactions
is nearly endless. This is because humans have prior knowledge about what
interactions are likely to be successful, i.e., to open a new door we first try
the handle. While learning such priors without supervision is easy for humans,
it is notoriously hard for machines. In this work, we tackle unsupervised
learning of priors of useful interactions with articulated objects, which we
call interaction modes. In contrast to the prior art, we use no supervision or
privileged information; we only assume access to the depth sensor in the
simulator to learn the interaction modes. More precisely, we define a
successful interaction as the one changing the visual environment substantially
and learn a generative model of such interactions, that can be conditioned on
the desired goal state of the object. In our experiments, we show that our
model covers most of the human interaction modes, outperforms existing
state-of-the-art methods for affordance learning, and can generalize to objects
never seen during training. Additionally, we show promising results in the
goal-conditional setup, where our model can be quickly fine-tuned to perform a
given task. We show in the experiments that such affordance learning predicts
interaction which covers most modes of interaction for the querying articulated
object and can be fine-tuned to a goal-conditional model. For supplementary:
https://actaim.github.io.
|
[
"cs.CV",
"cs.RO"
] | false |
2305.18361
|
2023-05-27T03:55:19Z
|
Deep learning network to correct axial and coronal eye motion in 3D OCT
retinal imaging
|
[
"Yiqian Wang",
"Alexandra Warter",
"Melina Cavichini",
"Varsha Alex",
"Dirk-Uwe G. Bartsch",
"William R. Freeman",
"Truong Q. Nguyen",
"Cheolhong An"
] |
Optical Coherence Tomography (OCT) is one of the most important retinal
imaging technique. However, involuntary motion artifacts still pose a major
challenge in OCT imaging that compromises the quality of downstream analysis,
such as retinal layer segmentation and OCT Angiography. We propose deep
learning based neural networks to correct axial and coronal motion artifacts in
OCT based on a single volumetric scan. The proposed method consists of two
fully-convolutional neural networks that predict Z and X dimensional
displacement maps sequentially in two stages. The experimental result shows
that the proposed method can effectively correct motion artifacts and achieve
smaller error than other methods. Specifically, the method can recover the
overall curvature of the retina, and can be generalized well to various
diseases and resolutions.
|
[
"eess.IV",
"cs.CV"
] | false |
2306.04644
|
2023-05-27T14:33:01Z
|
Decom--CAM: Tell Me What You See, In Details! Feature-Level
Interpretation via Decomposition Class Activation Map
|
[
"Yuguang Yang",
"Runtang Guo",
"Sheng Wu",
"Yimi Wang",
"Juan Zhang",
"Xuan Gong",
"Baochang Zhang"
] |
Interpretation of deep learning remains a very challenging problem. Although
the Class Activation Map (CAM) is widely used to interpret deep model
predictions by highlighting object location, it fails to provide insight into
the salient features used by the model to make decisions. Furthermore, existing
evaluation protocols often overlook the correlation between interpretability
performance and the model's decision quality, which presents a more fundamental
issue. This paper proposes a new two-stage interpretability method called the
Decomposition Class Activation Map (Decom-CAM), which offers a feature-level
interpretation of the model's prediction. Decom-CAM decomposes intermediate
activation maps into orthogonal features using singular value decomposition and
generates saliency maps by integrating them. The orthogonality of features
enables CAM to capture local features and can be used to pinpoint semantic
components such as eyes, noses, and faces in the input image, making it more
beneficial for deep model interpretation. To ensure a comprehensive comparison,
we introduce a new evaluation protocol by dividing the dataset into subsets
based on classification accuracy results and evaluating the interpretability
performance on each subset separately. Our experiments demonstrate that the
proposed Decom-CAM outperforms current state-of-the-art methods significantly
by generating more precise saliency maps across all levels of classification
accuracy. Combined with our feature-level interpretability approach, this paper
could pave the way for a new direction for understanding the decision-making
process of deep neural networks.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.17318
|
2023-05-27T00:47:39Z
|
Radar Enlighten the Dark: Enhancing Low-Visibility Perception for
Automated Vehicles with Camera-Radar Fusion
|
[
"Can Cui",
"Yunsheng Ma",
"Juanwu Lu",
"Ziran Wang"
] |
Sensor fusion is a crucial augmentation technique for improving the accuracy
and reliability of perception systems for automated vehicles under diverse
driving conditions. However, adverse weather and low-light conditions remain
challenging, where sensor performance degrades significantly, exposing vehicle
safety to potential risks. Advanced sensors such as LiDARs can help mitigate
the issue but with extremely high marginal costs. In this paper, we propose a
novel transformer-based 3D object detection model "REDFormer" to tackle low
visibility conditions, exploiting the power of a more practical and
cost-effective solution by leveraging bird's-eye-view camera-radar fusion.
Using the nuScenes dataset with multi-radar point clouds, weather information,
and time-of-day data, our model outperforms state-of-the-art (SOTA) models on
classification and detection accuracy. Finally, we provide extensive ablation
studies of each model component on their contributions to address the
above-mentioned challenges. Particularly, it is shown in the experiments that
our model achieves a significant performance improvement over the baseline
model in low-visibility scenarios, specifically exhibiting a 31.31% increase in
rainy scenes and a 46.99% enhancement in nighttime scenes.The source code of
this study is publicly available.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.17370
|
2023-05-27T05:09:03Z
|
Vision Transformers for Small Histological Datasets Learned through
Knowledge Distillation
|
[
"Neel Kanwal",
"Trygve Eftestol",
"Farbod Khoraminia",
"Tahlita CM Zuiverloon",
"Kjersti Engan"
] |
Computational Pathology (CPATH) systems have the potential to automate
diagnostic tasks. However, the artifacts on the digitized histological glass
slides, known as Whole Slide Images (WSIs), may hamper the overall performance
of CPATH systems. Deep Learning (DL) models such as Vision Transformers (ViTs)
may detect and exclude artifacts before running the diagnostic algorithm. A
simple way to develop robust and generalized ViTs is to train them on massive
datasets. Unfortunately, acquiring large medical datasets is expensive and
inconvenient, prompting the need for a generalized artifact detection method
for WSIs. In this paper, we present a student-teacher recipe to improve the
classification performance of ViT for the air bubbles detection task. ViT,
trained under the student-teacher framework, boosts its performance by
distilling existing knowledge from the high-capacity teacher model. Our
best-performing ViT yields 0.961 and 0.911 F1-score and MCC, respectively,
observing a 7% gain in MCC against stand-alone training. The proposed method
presents a new perspective of leveraging knowledge distillation over transfer
learning to encourage the use of customized transformers for efficient
preprocessing pipelines in the CPATH systems.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.17438
|
2023-05-27T10:26:23Z
|
On the Importance of Backbone to the Adversarial Robustness of Object
Detectors
|
[
"Xiao Li",
"Hang Chen",
"Xiaolin Hu"
] |
Object detection is a critical component of various security-sensitive
applications, such as autonomous driving and video surveillance. However,
existing deep learning-based object detectors are vulnerable to adversarial
attacks, which poses a significant challenge to their reliability and safety.
Through experiments, we found that existing works on improving the adversarial
robustness of object detectors have given a false sense of security. We argue
that using adversarially pre-trained backbone networks is essential for
enhancing the adversarial robustness of object detectors. We propose a simple
yet effective recipe for fast adversarial fine-tuning on object detectors with
adversarially pre-trained backbones. Without any modifications to the structure
of object detectors, our recipe achieved significantly better adversarial
robustness than previous works. Moreover, we explore the potential of different
modern object detectors to improve adversarial robustness using our recipe and
demonstrate several interesting findings. Our empirical results set a new
milestone and deepen the understanding of adversarially robust object
detection. Code and trained checkpoints will be publicly available.
|
[
"cs.CV",
"cs.AI",
"cs.CR",
"cs.LG"
] | false |
2305.17456
|
2023-05-27T12:12:53Z
|
Trustworthy Deep Learning for Medical Image Segmentation
|
[
"Lucas Fidon"
] |
Despite the recent success of deep learning methods at achieving new
state-of-the-art accuracy for medical image segmentation, some major
limitations are still restricting their deployment into clinics. One major
limitation of deep learning-based segmentation methods is their lack of
robustness to variability in the image acquisition protocol and in the imaged
anatomy that were not represented or were underrepresented in the training
dataset. This suggests adding new manually segmented images to the training
dataset to better cover the image variability. However, in most cases, the
manual segmentation of medical images requires highly skilled raters and is
time-consuming, making this solution prohibitively expensive. Even when
manually segmented images from different sources are available, they are rarely
annotated for exactly the same regions of interest. This poses an additional
challenge for current state-of-the-art deep learning segmentation methods that
rely on supervised learning and therefore require all the regions of interest
to be segmented for all the images to be used for training. This thesis
introduces new mathematical and optimization methods to mitigate those
limitations.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.17478
|
2023-05-27T13:49:35Z
|
Deep Variational Lesion-Deficit Mapping
|
[
"Guilherme Pombo",
"Robert Gray",
"Amy P. K. Nelson",
"Chris Foulon",
"John Ashburner",
"Parashkev Nachev"
] |
Causal mapping of the functional organisation of the human brain requires
evidence of \textit{necessity} available at adequate scale only from
pathological lesions of natural origin. This demands inferential models with
sufficient flexibility to capture both the observable distribution of
pathological damage and the unobserved distribution of the neural substrate.
Current model frameworks -- both mass-univariate and multivariate -- either
ignore distributed lesion-deficit relations or do not model them explicitly,
relying on featurization incidental to a predictive task. Here we initiate the
application of deep generative neural network architectures to the task of
lesion-deficit inference, formulating it as the estimation of an expressive
hierarchical model of the joint lesion and deficit distributions conditioned on
a latent neural substrate. We implement such deep lesion deficit inference with
variational convolutional volumetric auto-encoders. We introduce a
comprehensive framework for lesion-deficit model comparison, incorporating
diverse candidate substrates, forms of substrate interactions, sample sizes,
noise corruption, and population heterogeneity. Drawing on 5500 volume images
of ischaemic stroke, we show that our model outperforms established methods by
a substantial margin across all simulation scenarios, including comparatively
small-scale and noisy data regimes. Our analysis justifies the widespread
adoption of this approach, for which we provide an open source implementation:
https://github.com/guilherme-pombo/vae_lesion_deficit
|
[
"cs.LG",
"cs.CV",
"stat.AP",
"stat.ML"
] | false |
2305.17530
|
2023-05-27T17:16:27Z
|
PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
|
[
"Qingqing Cao",
"Bhargavi Paranjape",
"Hannaneh Hajishirzi"
] |
Large-scale vision language (VL) models use Transformers to perform
cross-modal interactions between the input text and image. These cross-modal
interactions are computationally expensive and memory-intensive due to the
quadratic complexity of processing the input image and text. We present PuMer:
a token reduction framework that uses text-informed Pruning and modality-aware
Merging strategies to progressively reduce the tokens of input image and text,
improving model inference speed and reducing memory footprint. PuMer learns to
keep salient image tokens related to the input text and merges similar textual
and visual tokens by adding lightweight token reducer modules at several
cross-modal layers in the VL model. Training PuMer is mostly the same as
finetuning the original VL model but faster. Our evaluation for two vision
language models on four downstream VL tasks shows PuMer increases inference
throughput by up to 2x and reduces memory footprint by over 50% while incurring
less than a 1% accuracy drop.
|
[
"cs.CV",
"cs.AI",
"cs.CL"
] | false |
2305.18367
|
2023-05-27T18:50:12Z
|
Using VGG16 Algorithms for classification of lung cancer in CT scans
Image
|
[
"Hasan Hejbari Zargar",
"Saha Hejbari Zargar",
"Raziye Mehri",
"Farzane Tajidini"
] |
Lung cancer is the leading reason behind cancer-related deaths within the
world. Early detection of lung nodules is vital for increasing the survival
rate of cancer patients. Traditionally, physicians should manually identify the
world suspected of getting carcinoma. When developing these detection systems,
the arbitrariness of lung nodules' shape, size, and texture could be a
challenge. Many studies showed the applied of computer vision algorithms to
accurate diagnosis and classification of lung nodules. A deep learning
algorithm called the VGG16 was developed during this paper to help medical
professionals diagnose and classify carcinoma nodules. VGG16 can classify
medical images of carcinoma in malignant, benign, and healthy patients. This
paper showed that nodule detection using this single neural network had 92.08%
sensitivity, 91% accuracy, and an AUC of 93%.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.19298
|
2023-05-27T20:44:14Z
|
MLOps: A Step Forward to Enterprise Machine Learning
|
[
"A. I. Ullah Tabassam"
] |
Machine Learning Operations (MLOps) is becoming a highly crucial part of
businesses looking to capitalize on the benefits of AI and ML models. This
research presents a detailed review of MLOps, its benefits, difficulties,
evolutions, and important underlying technologies such as MLOps frameworks,
Docker, GitHub actions, and Kubernetes. The MLOps workflow, which includes
model design, deployment, and operations, is explained in detail along with the
various tools necessary for both model and data exploration and deployment.
This article also puts light on the end-to-end production of ML projects using
various maturity levels of automated pipelines, with the least at no automation
at all and the highest with complete CI/CD and CT capabilities. Furthermore, a
detailed example of an enterprise-level MLOps project for an object detection
service is used to explain the workflow of the technology in a real-world
scenario. For this purpose, a web application hosting a pre-trained model from
TensorFlow 2 Model Zoo is packaged and deployed to the internet making sure
that the system is scalable, reliable, and optimized for deployment at an
enterprise level.
|
[
"cs.SE",
"cs.AI",
"cs.CV",
"cs.LG"
] | false |
2306.06078
|
2023-05-27T04:03:15Z
|
Cheating off your neighbors: Improving activity recognition through
corroboration
|
[
"Haoxiang Yu",
"Jingyi An",
"Evan King",
"Edison Thomaz",
"Christine Julien"
] |
Understanding the complexity of human activities solely through an
individual's data can be challenging. However, in many situations, surrounding
individuals are likely performing similar activities, while existing human
activity recognition approaches focus almost exclusively on individual
measurements and largely ignore the context of the activity. Consider two
activities: attending a small group meeting and working at an office desk. From
solely an individual's perspective, it can be difficult to differentiate
between these activities as they may appear very similar, even though they are
markedly different. Yet, by observing others nearby, it can be possible to
distinguish between these activities. In this paper, we propose an approach to
enhance the prediction accuracy of an individual's activities by incorporating
insights from surrounding individuals. We have collected a real-world dataset
from 20 participants with over 58 hours of data including activities such as
attending lectures, having meetings, working in the office, and eating
together. Compared to observing a single person in isolation, our proposed
approach significantly improves accuracy. We regard this work as a first step
in collaborative activity recognition, opening new possibilities for
understanding human activity in group settings.
|
[
"cs.CV",
"cs.HC",
"cs.LG",
"eess.SP"
] | false |
2305.18371
|
2023-05-27T23:08:22Z
|
ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing
UAV-Platform with Event-Based and Frame-Based Cameras
|
[
"Sizhen Bian",
"Lukas Schulthess",
"Georg Rutishauser",
"Alfio Di Mauro",
"Luca Benini",
"Michele Magno"
] |
The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles
(UAV) is raising, especially due to the microsecond-level reaction time of the
bio-inspired event sensor, which increases robustness and reduces latency of
the perception tasks compared to a RGB camera. This work presents ColibriUAV, a
UAV platform with both frame-based and event-based cameras interfaces for
efficient perception and near-sensor processing. The proposed platform is
designed around Kraken, a novel low-power RISC-V System on Chip with two
hardware accelerators targeting spiking neural networks and deep ternary neural
networks.Kraken is capable of efficiently processing both event data from a DVS
camera and frame data from an RGB camera. A key feature of Kraken is its
integrated, dedicated interface with a DVS camera. This paper benchmarks the
end-to-end latency and power efficiency of the neuromorphic and event-based UAV
subsystem, demonstrating state-of-the-art event data with a throughput of 7200
frames of events per second and a power consumption of 10.7 \si{\milli\watt},
which is over 6.6 times faster and a hundred times less power-consuming than
the widely-used data reading approach through the USB interface. The overall
sensing and processing power consumption is below 50 mW, achieving latency in
the milliseconds range, making the platform suitable for low-latency autonomous
nano-drones as well.
|
[
"cs.CV",
"cs.AI",
"cs.AR",
"cs.SY",
"eess.SY"
] | false |
2305.17325
|
2023-05-27T02:04:19Z
|
Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a
Solution
|
[
"Tianjian Li",
"Kenton Murray"
] |
Zero-shot cross-lingual transfer is when a multilingual model is trained to
perform a task in one language and then is applied to another language.
Although the zero-shot cross-lingual transfer approach has achieved success in
various classification tasks, its performance on natural language generation
tasks falls short in quality and sometimes outputs an incorrect language. In
our study, we show that the fine-tuning process learns language invariant
representations, which is beneficial for classification tasks but harmful for
generation tasks. Motivated by this, we propose a simple method to regularize
the model from learning language invariant representations and a method to
select model checkpoints without a development set in the target language, both
resulting in better generation quality. Experiments on three semantically
diverse generation tasks show that our method reduces the accidental
translation problem by 68% and improves the ROUGE-L score by 1.5 on average.
|
[
"cs.CL"
] | false |
2305.17347
|
2023-05-27T03:01:53Z
|
CGELBank Annotation Manual v1.0
|
[
"Brett Reynolds",
"Nathan Schneider",
"Aryaman Arora"
] |
CGELBank is a treebank and associated tools based on a syntactic formalism
for English derived from the Cambridge Grammar of the English Language. This
document lays out the particularities of the CGELBank annotation scheme.
|
[
"cs.CL"
] | false |
2305.17350
|
2023-05-27T03:06:15Z
|
How Good is Automatic Segmentation as a Multimodal Discourse Annotation
Aid?
|
[
"Corbyn Terpstra",
"Ibrahim Khebour",
"Mariah Bradford",
"Brett Wisniewski",
"Nikhil Krishnaswamy",
"Nathaniel Blanchard"
] |
Collaborative problem solving (CPS) in teams is tightly coupled with the
creation of shared meaning between participants in a situated, collaborative
task. In this work, we assess the quality of different utterance segmentation
techniques as an aid in annotating CPS. We (1) manually transcribe utterances
in a dataset of triads collaboratively solving a problem involving dialogue and
physical object manipulation, (2) annotate collaborative moves according to
these gold-standard transcripts, and then (3) apply these annotations to
utterances that have been automatically segmented using toolkits from Google
and OpenAI's Whisper. We show that the oracle utterances have minimal
correspondence to automatically segmented speech, and that automatically
segmented speech using different segmentation methods is also inconsistent. We
also show that annotating automatically segmented speech has distinct
implications compared with annotating oracle utterances--since most annotation
schemes are designed for oracle cases, when annotating automatically-segmented
utterances, annotators must invoke other information to make arbitrary
judgments which other annotators may not replicate. We conclude with a
discussion of how future annotation specs can account for these needs.
|
[
"cs.CL"
] | false |
2305.17351
|
2023-05-27T03:15:10Z
|
Disambiguated Lexically Constrained Neural Machine Translation
|
[
"Jinpeng Zhang",
"Nini Xiao",
"Ke Wang",
"Chuanqi Dong",
"Xiangyu Duan",
"Yuqi Zhang",
"Min Zhang"
] |
Lexically constrained neural machine translation (LCNMT), which controls the
translation generation with pre-specified constraints, is important in many
practical applications. Current approaches to LCNMT typically assume that the
pre-specified lexical constraints are contextually appropriate. This assumption
limits their application to real-world scenarios where a source lexicon may
have multiple target constraints, and disambiguation is needed to select the
most suitable one. In this paper, we propose disambiguated LCNMT (D-LCNMT) to
solve the problem. D-LCNMT is a robust and effective two-stage framework that
disambiguates the constraints based on contexts at first, then integrates the
disambiguated constraints into LCNMT. Experimental results show that our
approach outperforms strong baselines including existing data augmentation
based approaches on benchmark datasets, and comprehensive experiments in
scenarios where a source lexicon corresponds to multiple target constraints
demonstrate the constraint disambiguation superiority of our approach.
|
[
"cs.CL"
] | false |
2305.17356
|
2023-05-27T03:52:52Z
|
Bridging the Granularity Gap for Acoustic Modeling
|
[
"Chen Xu",
"Yuhao Zhang",
"Chengbo Jiao",
"Xiaoqian Liu",
"Chi Hu",
"Xin Zeng",
"Tong Xiao",
"Anxiang Ma",
"Huizhen Wang",
"JingBo Zhu"
] |
While Transformer has become the de-facto standard for speech, modeling upon
the fine-grained frame-level features remains an open challenge of capturing
long-distance dependencies and distributing the attention weights. We propose
\textit{Progressive Down-Sampling} (PDS) which gradually compresses the
acoustic features into coarser-grained units containing more complete semantic
information, like text-level representation. In addition, we develop a
representation fusion method to alleviate information loss that occurs
inevitably during high compression. In this way, we compress the acoustic
features into 1/32 of the initial length while achieving better or comparable
performances on the speech recognition task. And as a bonus, it yields
inference speedups ranging from 1.20$\times$ to 1.47$\times$. By reducing the
modeling burden, we also achieve competitive results when training on the more
challenging speech translation task.
|
[
"cs.CL"
] | false |
2305.17358
|
2023-05-27T03:54:09Z
|
CTC-based Non-autoregressive Speech Translation
|
[
"Chen Xu",
"Xiaoqian Liu",
"Xiaowen Liu",
"Qingxuan Sun",
"Yuhao Zhang",
"Murun Yang",
"Qianqian Dong",
"Tom Ko",
"Mingxuan Wang",
"Tong Xiao",
"Anxiang Ma",
"Jingbo Zhu"
] |
Combining end-to-end speech translation (ST) and non-autoregressive (NAR)
generation is promising in language and speech processing for their advantages
of less error propagation and low latency. In this paper, we investigate the
potential of connectionist temporal classification (CTC) for non-autoregressive
speech translation (NAST). In particular, we develop a model consisting of two
encoders that are guided by CTC to predict the source and target texts,
respectively. Introducing CTC into NAST on both language sides has obvious
challenges: 1) the conditional independent generation somewhat breaks the
interdependency among tokens, and 2) the monotonic alignment assumption in
standard CTC does not hold in translation tasks. In response, we develop a
prediction-aware encoding approach and a cross-layer attention approach to
address these issues. We also use curriculum learning to improve convergence of
training. Experiments on the MuST-C ST benchmarks show that our NAST model
achieves an average BLEU score of 29.5 with a speed-up of 5.67$\times$, which
is comparable to the autoregressive counterpart and even outperforms the
previous best result of 0.9 BLEU points.
|
[
"cs.CL"
] | false |
2305.17367
|
2023-05-27T04:47:09Z
|
Augmenting Large Language Model Translators via Translation Memories
|
[
"Yongyu Mu",
"Abudurexiti Reheman",
"Zhiquan Cao",
"Yuchun Fan",
"Bei Li",
"Yinqiao Li",
"Tong Xiao",
"Chunliang Zhang",
"Jingbo Zhu"
] |
Using translation memories (TMs) as prompts is a promising approach to
in-context learning of machine translation models. In this work, we take a step
towards prompting large language models (LLMs) with TMs and making them better
translators. We find that the ability of LLMs to ``understand'' prompts is
indeed helpful for making better use of TMs. Experiments show that the results
of a pre-trained LLM translator can be greatly improved by using high-quality
TM-based prompts. These results are even comparable to those of the
state-of-the-art NMT systems which have access to large-scale in-domain
bilingual data and are well tuned on the downstream tasks.
|
[
"cs.CL"
] | false |
2305.17371
|
2023-05-27T05:15:28Z
|
Towards Better Entity Linking with Multi-View Enhanced Distillation
|
[
"Yi Liu",
"Yuan Tian",
"Jianxun Lian",
"Xinlong Wang",
"Yanan Cao",
"Fang Fang",
"Wen Zhang",
"Haizhen Huang",
"Denvy Deng",
"Qi Zhang"
] |
Dense retrieval is widely used for entity linking to retrieve entities from
large-scale knowledge bases. Mainstream techniques are based on a dual-encoder
framework, which encodes mentions and entities independently and calculates
their relevances via rough interaction metrics, resulting in difficulty in
explicitly modeling multiple mention-relevant parts within entities to match
divergent mentions. Aiming at learning entity representations that can match
divergent mentions, this paper proposes a Multi-View Enhanced Distillation
(MVD) framework, which can effectively transfer knowledge of multiple
fine-grained and mention-relevant parts within entities from cross-encoders to
dual-encoders. Each entity is split into multiple views to avoid irrelevant
information being over-squashed into the mention-relevant view. We further
design cross-alignment and self-alignment mechanisms for this framework to
facilitate fine-grained knowledge distillation from the teacher model to the
student model. Meanwhile, we reserve a global-view that embeds the entity as a
whole to prevent dispersal of uniform information. Experiments show our method
achieves state-of-the-art performance on several entity linking benchmarks.
|
[
"cs.CL"
] | false |
2305.17404
|
2023-05-27T08:03:44Z
|
Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and
Spanish-Mixtec
|
[
"Atnafu Lambebo Tonja",
"Christian Maldonado-Sifuentes",
"David Alejandro Mendoza Castillo",
"Olga Kolesnikova",
"Noé Castro-Sánchez",
"Grigori Sidorov",
"Alexander Gelbukh"
] |
In this paper, we present a parallel Spanish-Mazatec and Spanish-Mixtec
corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two
indigenous Mexican languages. We evaluated the usability of the collected
corpus using three different approaches: transformer, transfer learning, and
fine-tuning pre-trained multilingual MT models. Fine-tuning the Facebook
M2M100-48 model outperformed the other approaches, with BLEU scores of 12.09
and 22.25 for Mazatec-Spanish and Spanish-Mazatec translations, respectively,
and 16.75 and 22.15 for Mixtec-Spanish and Spanish-Mixtec translations,
respectively. The findings show that the dataset size (9,799 sentences in
Mazatec and 13,235 sentences in Mixtec) affects translation performance and
that indigenous languages work better when used as target languages. The
findings emphasize the importance of creating parallel corpora for indigenous
languages and fine-tuning models for low-resource translation tasks. Future
research will investigate zero-shot and few-shot learning approaches to further
improve translation performance in low-resource settings. The dataset and
scripts are available at
\url{https://github.com/atnafuatx/Machine-Translation-Resources}
|
[
"cs.CL"
] | false |
2305.17406
|
2023-05-27T08:10:40Z
|
Enhancing Translation for Indigenous Languages: Experiments with
Multilingual Models
|
[
"Atnafu Lambebo Tonja",
"Hellina Hailu Nigatu",
"Olga Kolesnikova",
"Grigori Sidorov",
"Alexander Gelbukh",
"Jugal Kalita"
] |
This paper describes CIC NLP's submission to the AmericasNLP 2023 Shared Task
on machine translation systems for indigenous languages of the Americas. We
present the system descriptions for three methods. We used two multilingual
models, namely M2M-100 and mBART50, and one bilingual (one-to-one) -- Helsinki
NLP Spanish-English translation model, and experimented with different transfer
learning setups. We experimented with 11 languages from America and report the
setups we used as well as the results we achieved. Overall, the mBART setup was
able to improve upon the baseline for three out of the eleven languages.
|
[
"cs.CL"
] | false |
2305.17416
|
2023-05-27T08:42:37Z
|
A Practical Toolkit for Multilingual Question and Answer Generation
|
[
"Asahi Ushio",
"Fernando Alva-Manchego",
"Jose Camacho-Collados"
] |
Generating questions along with associated answers from a text has
applications in several domains, such as creating reading comprehension tests
for students, or improving document search by providing auxiliary questions and
answers based on the query. Training models for question and answer generation
(QAG) is not straightforward due to the expected structured output (i.e. a list
of question and answer pairs), as it requires more than generating a single
sentence. This results in a small number of publicly accessible QAG models. In
this paper, we introduce AutoQG, an online service for multilingual QAG, along
with lmqg, an all-in-one Python package for model fine-tuning, generation, and
evaluation. We also release QAG models in eight languages fine-tuned on a few
variants of pre-trained encoder-decoder language models, which can be used
online via AutoQG or locally via lmqg. With these resources, practitioners of
any level can benefit from a toolkit that includes a web interface for end
users, and easy-to-use code for developers who require custom models or
fine-grained controls for generation.
|
[
"cs.CL"
] | false |
2305.17440
|
2023-05-27T10:33:53Z
|
Modeling Adversarial Attack on Pre-trained Language Models as Sequential
Decision Making
|
[
"Xuanjie Fang",
"Sijie Cheng",
"Yang Liu",
"Wei Wang"
] |
Pre-trained language models (PLMs) have been widely used to underpin various
downstream tasks. However, the adversarial attack task has found that PLMs are
vulnerable to small perturbations. Mainstream methods adopt a detached
two-stage framework to attack without considering the subsequent influence of
substitution at each step. In this paper, we formally model the adversarial
attack task on PLMs as a sequential decision-making problem, where the whole
attack process is sequential with two decision-making problems, i.e., word
finder and word substitution. Considering the attack process can only receive
the final state without any direct intermediate signals, we propose to use
reinforcement learning to find an appropriate sequential attack path to
generate adversaries, named SDM-Attack. Extensive experimental results show
that SDM-Attack achieves the highest attack success rate with a comparable
modification rate and semantic similarity to attack fine-tuned BERT.
Furthermore, our analyses demonstrate the generalization and transferability of
SDM-Attack. The code is available at https://github.com/fduxuan/SDM-Attack.
|
[
"cs.CL"
] | false |
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