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What field is the article from? | Title: Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications
Abstract: Language serves as a vehicle for conveying thought, enabling communication
among individuals. The ability to distinguish between diverse concepts,
identify fairness and injustice, and comprehend a range of legal notio... | Computational Linguistics |
What field is the article from? | Title: Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach
Abstract: In this study, a Convolutional Neural Network (CNN) is used to classify
potato leaf illnesses using Deep Learning. The suggested approach entails
preprocessing the leaf image data, training a CNN model on th... | Computer Vision |
What field is the article from? | Title: Human-Centered Planning
Abstract: LLMs have recently made impressive inroads on tasks whose output is
structured, such as coding, robotic planning and querying databases. The vision
of creating AI-powered personal assistants also involves creating structured
outputs, such as a plan for one's day, or for an overs... | Artificial Intelligence |
What field is the article from? | Title: UFPS: A unified framework for partially-annotated federated segmentation in heterogeneous data distribution
Abstract: Partially supervised segmentation is a label-saving method based on datasets
with fractional classes labeled and intersectant. However, it is still far from
landing on real-world medical applicat... | Computer Vision |
What field is the article from? | Title: Symbolic Numeric Planning with Patterns
Abstract: In this paper, we propose a novel approach for solving linear numeric
planning problems, called Symbolic Pattern Planning. Given a planning problem
$\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions
-- we encode the problem of finding... | Artificial Intelligence |
What field is the article from? | Title: Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain
Abstract: The previous state-of-the-art (SOTA) method achieved a remarkable execution
accuracy on the Spider dataset, which is one of the largest and most diverse
datasets in the Text-... | Artificial Intelligence |
What field is the article from? | Title: Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
Abstract: This paper addresses the often overlooked issue of fairness in the autonomous
driving domain, particularly in vision-based perception and prediction systems,
which play a pivotal role in the overall functioning of Autono... | Computer Vision |
What field is the article from? | Title: Vision-Language Interpreter for Robot Task Planning
Abstract: Large language models (LLMs) are accelerating the development of
language-guided robot planners. Meanwhile, symbolic planners offer the
advantage of interpretability. This paper proposes a new task that bridges
these two trends, namely, multimodal pla... | Robotics |
What field is the article from? | Title: MOSEL: Inference Serving Using Dynamic Modality Selection
Abstract: Rapid advancements over the years have helped machine learning models reach
previously hard-to-achieve goals, sometimes even exceeding human capabilities.
However, to attain the desired accuracy, the model sizes and in turn their
computational r... | Machine Learning |
What field is the article from? | Title: Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
Abstract: We demonstrate a situation in which Large Language Models, trained to be
helpful, harmless, and honest, can display misaligned behavior and
strategically deceive their users about this behavior without... | Computational Linguistics |
What field is the article from? | Title: Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
Abstract: The advent of foundation models signals a new era in artificial intelligence.
The Segment Anything Model (SAM) is the first foundation model for image
segmentation. In this study, we evaluate SAM's ability to segment features... | Computer Vision |
What field is the article from? | Title: GLIME: General, Stable and Local LIME Explanation
Abstract: As black-box machine learning models grow in complexity and find applications
in high-stakes scenarios, it is imperative to provide explanations for their
predictions. Although Local Interpretable Model-agnostic Explanations (LIME)
[22] is a widely adpo... | Machine Learning |
What field is the article from? | Title: Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques
Abstract: Content-based image retrieval (CBIR) systems have emerged as crucial tools in
the field of computer vision, allowing for image search based on visual content
rather than relying solely on metadata. Th... | Computer Vision |
What field is the article from? | Title: Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space
Abstract: This article presents an AI-enabled Smart Video Surveillance (SVS) designed
to enhance safety in community spaces such as educational and recreational
areas, and small businesses. The proposed sy... | Computer Vision |
What field is the article from? | Title: Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model
Abstract: Health disparities are differences in health outcomes and access to
healthcare between different groups, including racial and ethnic minorities,
low-income people, and rural residents. An ar... | Computational Linguistics |
What field is the article from? | Title: Ethical implications of ChatGPT in higher education: A scoping review
Abstract: This scoping review explores the ethical challenges of using ChatGPT in
education, focusing particularly on issues related to higher education. By
reviewing recent academic articles written in English, Chinese, and Japanese,
we aimed... | Artificial Intelligence |
What field is the article from? | Title: Path Analysis for Effective Fault Localization in Deep Neural Networks
Abstract: Deep learning has revolutionized various real-world applications, but the
quality of Deep Neural Networks (DNNs) remains a concern. DNNs are complex and
have millions of parameters, making it difficult to determine their
contributio... | Artificial Intelligence |
What field is the article from? | Title: PhayaThaiBERT: Enhancing a Pretrained Thai Language Model with Unassimilated Loanwords
Abstract: While WangchanBERTa has become the de facto standard in transformer-based
Thai language modeling, it still has shortcomings in regard to the
understanding of foreign words, most notably English words, which are often... | Computational Linguistics |
What field is the article from? | Title: Dense X Retrieval: What Retrieval Granularity Should We Use?
Abstract: Dense retrieval has become a prominent method to obtain relevant context or
world knowledge in open-domain NLP tasks. When we use a learned dense retriever
on a retrieval corpus at inference time, an often-overlooked design choice is
the retr... | Computational Linguistics |
What field is the article from? | Title: Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content
Abstract: Amid growing global mental health concerns, particularly among vulnerable
groups, natural language processing offers a tremendous potential for early
detection and inter... | Machine Learning |
What field is the article from? | Title: Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction
Abstract: Large language models (LLMs) finetuned to follow human instruction have
recently exhibited significant capabilities in various English NLP tasks.
However, their performance in grammatical error correction (GEC), especially on
langu... | Computational Linguistics |
What field is the article from? | Title: Evaluating The Accuracy of Classification Algorithms for Detecting Heart Disease Risk
Abstract: The healthcare industry generates enormous amounts of complex clinical data
that make the prediction of disease detection a complicated process. In medical
informatics, making effective and efficient decisions is very... | Machine Learning |
What field is the article from? | Title: Outcome-supervised Verifiers for Planning in Mathematical Reasoning
Abstract: Large language models (LLMs) often struggle with maintaining accuracy across
a sequence of intermediate reasoning steps in mathematical reasoning, leading
to error propagation that undermines the final result. The current methodology
t... | Artificial Intelligence |
What field is the article from? | Title: Self Attention with Temporal Prior: Can We Learn More from Arrow of Time?
Abstract: Many of diverse phenomena in nature often inherently encode both short and
long term temporal dependencies, short term dependencies especially resulting
from the direction of flow of time. In this respect, we discovered experimen... | Artificial Intelligence |
What field is the article from? | Title: HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
Abstract: Existing open-source helpfulness preference datasets do not specify what
makes some responses more helpful and others less so. Models trained on these
datasets can incidentally learn to model dataset artifacts (e.g. preferring
longer but unhelp... | Computational Linguistics |
What field is the article from? | Title: ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Abstract: Large language model (LLM) prompting is a promising new approach for users to
create and customize their own chatbots. However, current methods for steering
a chatbot's outputs, such as prompt engin... | Human-Computer Interaction |
What field is the article from? | Title: The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4
Abstract: In recent years, groundbreaking advancements in natural language processing
have culminated in the emergence of powerful large language models (LLMs),
which have showcased remarkable capabilities across a vast ... | Computational Linguistics |
What field is the article from? | Title: Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix
Abstract: In multilingual translation research, the comprehension and utilization of
language families are of paramount importance. Nevertheless, clustering
languages based solely on their ancestral families can y... | Computational Linguistics |
What field is the article from? | Title: Learning to Filter Context for Retrieval-Augmented Generation
Abstract: On-the-fly retrieval of relevant knowledge has proven an essential element of
reliable systems for tasks such as open-domain question answering and fact
verification. However, because retrieval systems are not perfect, generation
models are ... | Computational Linguistics |
What field is the article from? | Title: Quantifying the redundancy between prosody and text
Abstract: Prosody -- the suprasegmental component of speech, including pitch, loudness,
and tempo -- carries critical aspects of meaning. However, the relationship
between the information conveyed by prosody vs. by the words themselves remains
poorly understood... | Computational Linguistics |
What field is the article from? | Title: HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Abstract: Model pre-training is essential in human-centric perception. In this paper,
we first introduce masked image modeling (MIM) as a pre-training approach for
this task. Upon revisiting the MIM training strategy, we reveal that human
st... | Computer Vision |
What field is the article from? | Title: GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
Abstract: Massive transformer-based models face several challenges, including slow and
computationally intensive pre-training and over-parametrization. This paper
addresses these challenges by proposing a versatile method called ... | Machine Learning |
What field is the article from? | Title: TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine
Abstract: Pre-training and fine-tuning have emerged as a promising paradigm across
various natural language processing (NLP) tasks. The effectiveness of
pretrained large language models (LLM) has witnes... | Computational Linguistics |
What field is the article from? | Title: Prediction of rare events in the operation of household equipment using co-evolving time series
Abstract: In this study, we propose an approach for predicting rare events by
exploiting time series in coevolution. Our approach involves a weighted
autologistic regression model, where we leverage the temporal behav... | Machine Learning |
What field is the article from? | Title: Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking
Abstract: Standard Full-Data classifiers in NLP demand thousands of labeled examples,
which is impractical in data-limited domains. Few-shot methods offer an
alternative, utilizing contrastive learning techniques that can be effective... | Computational Linguistics |
What field is the article from? | Title: Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk
Abstract: Applications of large language models (LLMs) like ChatGPT have potential to
enhance clinical decision support through conversational interfaces. However,
challenges of huma... | Human-Computer Interaction |
What field is the article from? | Title: Can LLMs Follow Simple Rules?
Abstract: As Large Language Models (LLMs) are deployed with increasing real-world
responsibilities, it is important to be able to specify and constrain the
behavior of these systems in a reliable manner. Model developers may wish to
set explicit rules for the model, such as "do not ... | Artificial Intelligence |
What field is the article from? | Title: Deeper Understanding of Black-box Predictions via Generalized Influence Functions
Abstract: Influence functions (IFs) elucidate how learning data affects model behavior.
However, growing non-convexity and the number of parameters in modern
large-scale models lead to imprecise influence approximation and instabil... | Machine Learning |
What field is the article from? | Title: gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation
Abstract: Binary semantic segmentation in computer vision is a fundamental problem. As
a model-based segmentation method, the graph-cut approach was one of the most
successful binary segmentation methods thanks to its global optim... | Computer Vision |
What field is the article from? | Title: Improving Fairness using Vision-Language Driven Image Augmentation
Abstract: Fairness is crucial when training a deep-learning discriminative model,
especially in the facial domain. Models tend to correlate specific
characteristics (such as age and skin color) with unrelated attributes
(downstream tasks), result... | Computer Vision |
What field is the article from? | Title: Personalized Speech-driven Expressive 3D Facial Animation Synthesis with Style Control
Abstract: Different people have different facial expressions while speaking
emotionally. A realistic facial animation system should consider such
identity-specific speaking styles and facial idiosyncrasies to achieve
high-degr... | Artificial Intelligence |
What field is the article from? | Title: Human-centred explanation of rule-based decision-making systems in the legal domain
Abstract: We propose a human-centred explanation method for rule-based automated
decision-making systems in the legal domain. Firstly, we establish a conceptual
framework for developing explanation methods, representing its key i... | Artificial Intelligence |
What field is the article from? | Title: BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
Abstract: The ability to accurately predict the trajectory of surrounding vehicles is a
critical hurdle to overcome on the journey to fully autonomous vehicles. To
address this challenge, we pioneer a novel behavior-aware trajectory pred... | Robotics |
What field is the article from? | Title: Do large language models solve verbal analogies like children do?
Abstract: Analogy-making lies at the heart of human cognition. Adults solve analogies
such as \textit{Horse belongs to stable like chicken belongs to ...?} by
mapping relations (\textit{kept in}) and answering \textit{chicken coop}. In
contrast, c... | Computational Linguistics |
What field is the article from? | Title: Knowledge Corpus Error in Question Answering
Abstract: Recent works in open-domain question answering (QA) have explored generating
context passages from large language models (LLMs), replacing the traditional
retrieval step in the QA pipeline. However, it is not well understood why
generated passages can be mor... | Computational Linguistics |
What field is the article from? | Title: Using Large Language Models for Hyperparameter Optimization
Abstract: This paper studies using foundational large language models (LLMs) to make
decisions during hyperparameter optimization (HPO). Empirical evaluations
demonstrate that in settings with constrained search budgets, LLMs can perform
comparably or b... | Machine Learning |
What field is the article from? | Title: Inherent limitations of LLMs regarding spatial information
Abstract: Despite the significant advancements in natural language processing
capabilities demonstrated by large language models such as ChatGPT, their
proficiency in comprehending and processing spatial information, especially
within the domains of 2D a... | Computational Linguistics |
What field is the article from? | Title: AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Abstract: Safety alignment of Large Language Models (LLMs) can be compromised with
manual jailbreak attacks and (automatic) adversarial attacks. Recent studies
suggest that defending against these attacks is possible: adversarial ... | Cryptography and Security |
What field is the article from? | Title: Evaluation of large language models using an Indian language LGBTI+ lexicon
Abstract: Large language models (LLMs) are typically evaluated on the basis of
task-based benchmarks such as MMLU. Such benchmarks do not examine responsible
behaviour of LLMs in specific contexts. This is particularly true in the LGBTI+... | Computational Linguistics |
What field is the article from? | Title: Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts
Abstract: Zero-shot Visual Question Answering (VQA) is a prominent vision-language task
that examines both the visual and textual understanding capability of systems
in the absence of training data. Recently, ... | Computer Vision |
What field is the article from? | Title: Exploring Automatic Text Simplification of German Narrative Documents
Abstract: In this paper, we apply transformer-based Natural Language Generation (NLG)
techniques to the problem of text simplification. Currently, there are only a
few German datasets available for text simplification, even fewer with larger
a... | Computational Linguistics |
What field is the article from? | Title: Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Abstract: Clinical natural language processing requires methods that can address
domain-specific challenges, such as complex medical terminology and clinical
contexts. Recently, large language models (LL... | Computational Linguistics |
What field is the article from? | Title: MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters
Abstract: Most deep neural networks are trained under fixed network architectures and
require retraining when the architecture changes. If expanding the network's
size is needed, it is necessary to retrain from scratch, which is expensive. ... | Machine Learning |
What field is the article from? | Title: EpiK-Eval: Evaluation for Language Models as Epistemic Models
Abstract: In the age of artificial intelligence, the role of large language models
(LLMs) is becoming increasingly central. Despite their growing prevalence,
their capacity to consolidate knowledge from different training documents - a
crucial ability... | Computational Linguistics |
What field is the article from? | Title: Interpretable pap smear cell representation for cervical cancer screening
Abstract: Screening is critical for prevention and early detection of cervical cancer
but it is time-consuming and laborious. Supervised deep convolutional neural
networks have been developed to automate pap smear screening and the results... | Computer Vision |
What field is the article from? | Title: Debate Helps Supervise Unreliable Experts
Abstract: As AI systems are used to answer more difficult questions and potentially
help create new knowledge, judging the truthfulness of their outputs becomes
more difficult and more important. How can we supervise unreliable experts,
which have access to the truth but... | Artificial Intelligence |
What field is the article from? | Title: Online Vectorized HD Map Construction using Geometry
Abstract: The construction of online vectorized High-Definition (HD) maps is critical
for downstream prediction and planning. Recent efforts have built strong
baselines for this task, however, shapes and relations of instances in urban
road systems are still u... | Computer Vision |
What field is the article from? | Title: DDxT: Deep Generative Transformer Models for Differential Diagnosis
Abstract: Differential Diagnosis (DDx) is the process of identifying the most likely
medical condition among the possible pathologies through the process of
elimination based on evidence. An automated process that narrows a large set of
patholog... | Machine Learning |
What field is the article from? | Title: EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that
enables collaborative model training across dispersed nodes without having to
force individual nodes to share data. However, its broad adop... | Machine Learning |
What field is the article from? | Title: Online Continual Knowledge Learning for Language Models
Abstract: Large Language Models (LLMs) serve as repositories of extensive world
knowledge, enabling them to perform tasks such as question-answering and
fact-checking. However, this knowledge can become obsolete as global contexts
change. In this paper, we ... | Computational Linguistics |
What field is the article from? | Title: Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
Abstract: Inventory Routing Problem (IRP) is a crucial challenge in supply chain
management as it involves optimizing efficient route selection while
considering the uncertainty of inventory demand planning. To solve IRPs,
u... | Machine Learning |
What field is the article from? | Title: Non-autoregressive Machine Translation with Probabilistic Context-free Grammar
Abstract: Non-autoregressive Transformer(NAT) significantly accelerates the inference
of neural machine translation. However, conventional NAT models suffer from
limited expression power and performance degradation compared to autoreg... | Computational Linguistics |
What field is the article from? | Title: Adversarial Examples in the Physical World: A Survey
Abstract: Deep neural networks (DNNs) have demonstrated high vulnerability to
adversarial examples. Besides the attacks in the digital world, the practical
implications of adversarial examples in the physical world present significant
challenges and safety con... | Computer Vision |
What field is the article from? | Title: Evolving Reservoirs for Meta Reinforcement Learning
Abstract: Animals often demonstrate a remarkable ability to adapt to their environments
during their lifetime. They do so partly due to the evolution of morphological
and neural structures. These structures capture features of environments shared
between genera... | Machine Learning |
What field is the article from? | Title: Improving Real Estate Appraisal with POI Integration and Areal Embedding
Abstract: Despite advancements in real estate appraisal methods, this study primarily
focuses on two pivotal challenges. Firstly, we explore the often-underestimated
impact of Points of Interest (POI) on property values, emphasizing the
nec... | Artificial Intelligence |
What field is the article from? | Title: Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks
Abstract: Instruction tuning (IT) achieves impressive zero-shot generalization results
by training large language models (LLMs) on a massive amount of diverse tasks
with instructions. However, how to select new t... | Computational Linguistics |
What field is the article from? | Title: Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?
Abstract: Ontologies play a crucial role in organizing and representing knowledge.
However, even current ontologies do not encompass all relevant concepts and
relationships. Here, we explore the potential of large langua... | Computational Linguistics |
What field is the article from? | Title: Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
Abstract: We present BYOKG, a universal question-answering (QA) system that can operate
on any knowledge graph (KG), requires no human-annotated training data, and can
be ready to use within a day -- attributes that are out-of-scope for curr... | Computational Linguistics |
What field is the article from? | Title: Evolutionary City: Towards a Flexible, Agile and Symbiotic System
Abstract: Urban growth sometimes leads to rigid infrastructure that struggles to adapt
to changing demand. This paper introduces a novel approach, aiming to enable
cities to evolve and respond more effectively to such dynamic demand. It
identifies... | Computers and Society |
What field is the article from? | Title: Improved Face Representation via Joint Label Classification and Supervised Contrastive Clustering
Abstract: Face clustering tasks can learn hierarchical semantic information from
large-scale data, which has the potential to help facilitate face recognition.
However, there are few works on this problem. This pape... | Computer Vision |
What field is the article from? | Title: Vision Encoder-Decoder Models for AI Coaching
Abstract: This research paper introduces an innovative AI coaching approach by
integrating vision-encoder-decoder models. The feasibility of this method is
demonstrated using a Vision Transformer as the encoder and GPT-2 as the
decoder, achieving a seamless integrati... | Computer Vision |
What field is the article from? | Title: Solving large flexible job shop scheduling instances by generating a diverse set of scheduling policies with deep reinforcement learning
Abstract: The Flexible Job Shop Scheduling Problem (FJSSP) has been extensively studied
in the literature, and multiple approaches have been proposed within the
heuristic, exac... | Artificial Intelligence |
What field is the article from? | Title: Can ChatGPT support software verification?
Abstract: Large language models have become increasingly effective in software
engineering tasks such as code generation, debugging and repair. Language
models like ChatGPT can not only generate code, but also explain its inner
workings and in particular its correctness... | Software Engineering |
What field is the article from? | Title: All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Abstract: Public opinion is shaped by the information news media provide, and that
information in turn may be shaped by the ideological preferences of media
outlets. But while much attention has been devoted to media b... | Computational Linguistics |
What field is the article from? | Title: Levels of AGI: Operationalizing Progress on the Path to AGI
Abstract: We propose a framework for classifying the capabilities and behavior of
Artificial General Intelligence (AGI) models and their precursors. This
framework introduces levels of AGI performance, generality, and autonomy. It is
our hope that this ... | Artificial Intelligence |
What field is the article from? | Title: Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters
Abstract: Coding of unstructured clinical free-text to produce interoperable structured
data is essential to improve direct care, support clinical communication and to
enable c... | Artificial Intelligence |
What field is the article from? | Title: Assessing AI Impact Assessments: A Classroom Study
Abstract: Artificial Intelligence Impact Assessments ("AIIAs"), a family of tools that
provide structured processes to imagine the possible impacts of a proposed AI
system, have become an increasingly popular proposal to govern AI systems.
Recent efforts from go... | Computers and Society |
What field is the article from? | Title: CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation
Abstract: Current text-to-image editing models often encounter challenges with smoothly
manipulating multiple attributes using a single instruction. Taking inspiration
from the Chain-of-Thought prompting technique utilized in language models, ... | Computer Vision |
What field is the article from? | Title: BCN: Batch Channel Normalization for Image Classification
Abstract: Normalization techniques have been widely used in the field of deep learning
due to their capability of enabling higher learning rates and are less careful
in initialization. However, the effectiveness of popular normalization
technologies is ty... | Computer Vision |
What field is the article from? | Title: Woodpecker: Hallucination Correction for Multimodal Large Language Models
Abstract: Hallucination is a big shadow hanging over the rapidly evolving Multimodal
Large Language Models (MLLMs), referring to the phenomenon that the generated
text is inconsistent with the image content. In order to mitigate
hallucinat... | Computer Vision |
What field is the article from? | Title: Decoupled DETR For Few-shot Object Detection
Abstract: Few-shot object detection (FSOD), an efficient method for addressing the
severe data-hungry problem, has been extensively discussed. Current works have
significantly advanced the problem in terms of model and data. However, the
overall performance of most FS... | Computer Vision |
What field is the article from? | Title: VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Abstract: Understanding how humans leverage semantic knowledge to navigate unfamiliar
environments and decide where to explore next is pivotal for developing robots
capable of human-like search behaviors. We introduce a zero-shot navigation
ap... | Robotics |
What field is the article from? | Title: Simplifying Neural Network Training Under Class Imbalance
Abstract: Real-world datasets are often highly class-imbalanced, which can adversely
impact the performance of deep learning models. The majority of research on
training neural networks under class imbalance has focused on specialized loss
functions, samp... | Machine Learning |
What field is the article from? | Title: SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Abstract: Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to
extend the in-domain model to the distinctive target domains where the data
distributions differ. Most prior works focus on capturing the inter-domain
transfer... | Computer Vision |
What field is the article from? | Title: Post-Training Quantization with Low-precision Minifloats and Integers on FPGAs
Abstract: Post-Training Quantization (PTQ) is a powerful technique for model
compression, reducing the precision of neural networks without additional
training overhead. Recent works have investigated adopting 8-bit floating-point
qua... | Computer Vision |
What field is the article from? | Title: Incorporating Worker Perspectives into MTurk Annotation Practices for NLP
Abstract: Current practices regarding data collection for natural language processing
on Amazon Mechanical Turk (MTurk) often rely on a combination of studies on
data quality and heuristics shared among NLP researchers. However, without
co... | Computational Linguistics |
What field is the article from? | Title: Data-Free Distillation of Language Model by Text-to-Text Transfer
Abstract: Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the
model when original training data is unavailable. Previous works for DFKD in
NLP mainly focus on distilling encoder-only structures like BERT on
classification... | Computational Linguistics |
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