Singapore

Recent work sheds light on the interests of Machine
Learning (ML) learners, both to support their success and
to broaden participation. Yet, do learning resources like
textbooks ‘fit’ with these interests? We analyzed
introductions of ten openly available ML textbooks using
thematic analysis to identify motivational strategies and
compare them with student interests in ML reported in prior
work. We found that textbooks frequently motivated learners
by setting learning goals, previewing core ML topics (e.g.,
classification, regression), showcasing applications and
current successes, and, less often, by using
learner-centered strategies such as reassurance or
curiosity prompts. We grouped these into three overarching
themes: theoretical, practical, and learner-centered. These
motivations largely align with student interest,
particularly in theory and applications, even in textbooks
published before the recent surge of ML and Artificial
Intelligence (AI). Our findings offer insight into how
textbooks frame the value of ML and provide guidance for
creating future materials that engage and support a wider
range of learners.

AAAI 2026

Analysis of Motivations in Machine Learning Textbooks

textbook analysis

machine learning education

motivation

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

I am writing to express my interest in being considered for
the AAAI 2026 New Faculty Highlight program. In Fall
2025, I began my second year as an Assistant Professor
at the University of Virginia, where I conduct research in
robotics, artificial intelligence (AI), and multi-agent
systems. My work has received several international awards,
including Best Paper, Dissertation, and Young Investigator
honors. It has over 3, 184 citations on Google Scholar
with an H-index of 23, and has been published in leading
venues such as CVPR, ICCV, AAAI, IJCAI, and major
robotics conferences. My research has been downloaded,
reproduced, and applied by scholars worldwide. I believe my
expertise, research trajectory, and future goals align with
the
objectives of this program.

Towards Agents That Exhibit Human-Like Autonomy in Complex Environments

Neural networks are a powerful tool for learning patterns from data. However, they do not respect known scientific laws, nor can they reveal novel scientific insights due to their black-box nature. In contrast, scientific reasoning distills biological or physical principles from observations and controlled experiments, and quantitatively interprets them with process-based models made of mathematical equations. Yet, process-based models rely on numerous free parameters that must be set in an ad-hoc manner, and thus often fit observations poorly in cross-scale predictions. While prior work has embedded process-based models in conventional neural networks, discovering interpretable relationships between parameters in process-based models and input features is still a grand challenge for scientific discovery. We thus propose Scientifically-Interpretable Reasoning Network (ScIReN), a fully-transparent framework that combines interpretable neural and process-based reasoning. An interpretable encoder predicts scientifically-meaningful latent parameters, which are then passed through a differentiable process-based decoder to predict labeled output variables. ScIReN leverages Kolmogorov-Arnold networks (KAN) to ensure the encoder is fully interpretable and reveals relationships between input features and latent parameters; it uses smoothness penalties to balance expressivity and simplicity. ScIReN also uses a novel hard-sigmoid constraint layer to restrict latent parameters to meaningful ranges defined by scientific prior knowledge, further enhancing its interpretability. While the embedded process-based model enforces established scientific knowledge, the KAN-based encoder reveals new scientific mechanisms and relationships hidden in conventional black-box models. We apply ScIReN on two tasks: simulating the flow of organic carbon through soils, and modeling ecosystem respiration from plants. In both tasks, ScIReN outperforms black-box networks in predictive accuracy while providing substantial scientific interpretability -- it can infer latent scientific mechanisms and their relationships with input features. These tasks are particularly important for mitigating climate change -- soils can sequester carbon from the atmosphere, but we have a very limited understanding of how carbon flows through these soils: ScIReN combines the best scientific knowledge with interpretable learning to help scientists gain insight into these intricate processes.

Scientifically-Interpretable Reasoning Network (ScIReN): Discovering Hidden Relationships in the Carbon Cycle and Beyond

Undergraduate research experiences are often limited to
small-scale apprenticeship models, leaving many students
without accessible entry points into research practice.
This paper presents the design and evaluation of a
semester-long course for undergraduates to gain research
experience in Machine Learning. The course, led by one
faculty instructor, enables nearly a hundred students to
engage in structured research through a scaffolded
replication-and-extension project, where students first
replicate a published research project and then implement
novel additions. The course integrates instructional
modules (e.g., guided paper reading, proposal writing,
public presentation) with project milestones (e.g.,
replication, extension, poster) to support research
learning for students with diverse backgrounds. Every
component of research is visited several times, with each
iteration having progressively increased autonomy coupled
with simultaneously decreased scaffolding. We find that the
scaffolding modules help students develop foundational
conceptual and procedural understanding of doing research,
and the project milestones on replication and extension
help them gain execution skills gradually. Students also
report developing a researcher mindset and feeling like
they understand the research process better.
We discuss the principles used to design a scalable
research-based course: balancing scaffolding to provide
foundational understanding with autonomy for students to
“feel like real researchers”.

Balancing Scaffolding and Autonomy: A Case Study in Designing a Scalable Undergraduate Machine Learning Research Course

As machine learning (ML) becomes integral in more
disciplines, introductory courses in the field are
attracting increasingly diverse audiences. Design of these
introductory ML courses needs to be theoretically sound,
but also intuitive, engaging, and accessible to a range of
students. Effective teaching of ML must go beyond teaching
the theoretical or practical mechanics of algorithms. In
this paper, we synthesize effective teaching strategies
from 6 experienced ML instructors across 5 institutions to
help students define appropriate ML problems, build
intuition, develop reasoning skills, and apply models
responsibly. We organize these strategies into eight
thematic areas: preparing students for success, motivating
learners through real-world relevance, integrating ethics
and societal impact, avoiding common methodological
pitfalls in model evaluation, guiding students on design
decisions, adapting effective classroom practices,
assessing student learning, and preparing for the future.
Each section offers practical examples of classroom-tested
activities (or references to existing resources), and in
many cases, reflections on our experiences with the
strategies. Our aim is for this paper to be a starting
point for instructors aiming to improve learning in
introductory ML courses. We hope this is a resource-rich
guide for teaching ML to diverse learners, grounded in both
pedagogy and practice.

Effective Strategies for Teaching Machine Learning

This paper investigates the detection of misinformation, which deceives readers by explicitly fabricating misleading content or implicitly omitting important information necessary for informed judgment. While the former has been extensively studied, omission-based deception remains largely overlooked, even though it can subtly guide readers toward false conclusions under the illusion of completeness. To pioneer in this field, this paper presents OmiGraph, the first omission-aware framework for misinformation detection. Specifically, OmiGraph constructs an omission-aware graph for the target news by utilizing a contextual environment that captures complementary perspectives of the same event, thereby surfacing potentially omitted contents. Based on this graph, omission-oriented relation modeling is then proposed to identify the internal contextual dependencies, as well as the dynamic omission intents, formulating a comprehensive omission relation representation. Finally, to extract omission patterns for detection, OmiGraph introduces omission-aware message-passing and aggregation that establishes holistic deception perception by integrating the omission contents and relations. Experiments show that, by considering the omission perspective, our approach attains remarkable performance, achieving average improvements of +5.4% F1 and +5.3% ACC on two large-scale benchmarks.

Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference

Vision-Language Models (VLMs) are widely used in tasks like Open-Vocabulary Object Detection and zero-shot Classification, owing to their powerful generalization. However, recent research reveals that VLMs exhibit significant performance instability when tasked with recognizing concepts at varying granularities (\textit{e.g.}, animal vs. dog). Prevailing methods inject external knowledge from Large Language Models, but this unconstrained approach distorts the VLM's inherent hierarchical orthogonal geometry, leading to performance collapse on general concepts. To address this, we introduce \textbf{\textit{GeCoin}}, an innovative \textbf{\textit{Ge}}ometrically \textbf{\textit{Co}}nstra\textbf{\textit{in}}ed framework that safely enhances existing VLMs with external knowledge for improved hierarchical understanding, without additional training. By projecting knowledge into the null-space of a query concept's feature space, \textit{GeCoin} mathematically guarantees the preservation of general knowledge while integrating specialized information. Extensive experiments across large-scale benchmarks, diverse VLMs (\textit{e.g.}, CLIP, SigLip 2), and knowledge from various LLMs (\textit{e.g.}, GPT-3.5, Claude-3, Gemini-Pro) show that \textbf{GeCoin} boosts performance by an average of 3.9\% over the strongest baseline—crucially eradicating performance collapse on general concepts. The code link is in the supplementary material.

Injection Without Distortion: Geometrically Constrained Knowledge Enhancement for Vision-Language Models

Tourism and travel planning increasingly rely on digital assistance, yet existing multimodal AI systems often lack specialized knowledge and contextual understanding of urban environments. We present TraveLLaMA, a specialized multimodal language model designed for comprehensive travel assistance. Our work addresses the fundamental challenge of developing practical AI travel assistants through three key contributions: (1) TravelQA, a novel dataset of 265k question-answer pairs combining 160k text QA from authentic travel sources, 100k vision-language QA featuring maps and location imagery, and 5k expert-annotated Chain-of-Thought reasoning examples; (2) Travel-CoT, a structured reasoning framework that decomposes travel queries into spatial, temporal, and practical dimensions, improving answer accuracy by 10.8% while providing interpretable decision paths; and (3) an interactive agent system validated through extensive user studies. Through fine-tuning experiments on state-of-the-art vision-language models (LLaVA, Qwen-VL, Shikra), we achieve 6.2-9.4% base improvements, further enhanced by Travel-CoT reasoning. Our model demonstrates superior capabilities in contextual travel recommendations, map interpretation, and scene understanding while providing practical information such as operating hours and cultural insights. User studies with 500 participants show TraveLLaMA achieves a System Usability Scale score of 82.5, significantly outperforming general-purpose models and establishing new standards for multimodal travel assistance systems.

TraveLLaMA: A Multimodal Travel Assistant with Large-Scale Dataset and Structured Reasoning

The neural-enhanced video streaming (NeVS) has been an emerging technique to integrate neural models into video codecs for higher streaming efficiency. The state-of-the-art methods, e.g., DeNC and Gemino, typically compress videos in RGB space and restore video quality via a neural enhancement model hosted on the external media server. However, these methods are not always accessible in resource-constrained edge environments due to their heavy reliance on the media server's computation, which undermines end-to-end performance and restricts NeVS's usage boundary. This limitation raises an interesting question: is it possible to make NeVS lightweight so that all neural codec operations can be handled directly by clients' edge devices? In this paper, we present the answer yes and develop a new plug-and-play module called DeNC++, which significantly improves the compression-restoration-overhead trade-off over existing methods. Our core design philosophy is to wrap all the codec operations within a latent semantic space, in which the original high-dimensional visual signals are efficiently embedded into low-dimensional semantic representations. With this fundamental transformation, DeNC++'s neural encoder introduces the triple semantic-bitwidth-resolution compression to effectively lower the streaming traffic. Meanwhile, we make DeNC++'s neural decoder aware of the perceptual loss caused by its encoder and design tiny generative models to guarantee high restoration quality. We also strictly restrict the runtime computational overhead and accelerate the neural enhancement process, making DeNC++ compatible with commodity edge devices. Real-world evaluations reveal that DeNC++ consistently provides higher restoration quality while achieving 24-55 times higher compression ratio and 5-7 times end-to-end speedup over the latest NeVS solutions.

DeNC++: Efficient Diffusion-Enhanced Neural Codec for End-to-end Semantic Streaming at the Edge

Graph Neural Networks (GNNs) address two key challenges in applying deep learning to graph-structured data: they handle varying size input graphs and ensure invariance under graph isomorphism. While GNNs have demonstrated broad applicability, understanding their expressive power remains an important question. In this paper, we propose GNN architectures that correspond precisely to prominent fragments of first-order logic (FO), including various modal logics as well as more expressive two-variable fragments. To establish these results, we apply methods from finite model theory of first-order and modal logics to the domain of graph representation learning. Our results provide a unifying framework for understanding the logical expressiveness of GNNs within FO.

The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic

Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Divide-and-Conquer Decoupled Network (DCDNet). In the training stage, to tackle feature entanglement that impedes cross-domain generalization and rapid adaptation, we propose the Adversarial-Contrastive Feature Decomposition (ACFD) module. It decouples backbone features into category-relevant private and domain-relevant shared representations via contrastive learning and adversarial learning. Then, to mitigate the potential degradation caused by the disentanglement, the Matrix-Guided Dynamic Fusion (MGDF) module adaptively integrates base, shared, and private features under spatial guidance, maintaining structural coherence. In addition, in the fine-tuning stage, to enhanced model generalization, the Cross-Adaptive Modulation (CAM) module is placed before the MGDF, where shared features guide private features via modulation ensuring effective integration of domain-relevant information. Extensive experiments on four challenging datasets show that DCDNet outperforms existing CD-FSS methods, setting a new state-of-the-art for cross-domain generalization and few-shot adaptation. Code: https://github.com/rawwap/DCDNet.

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