article stringlengths 507 295k | abstract stringlengths 417 1.92k | category sequencelengths 1 6 |
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# 1 Introduction
Modern Artificial Neural Networks (ANNs) achieve remarkable results in recognizing patterns. However, due to their complexity and black-box character, their failures are hard to identify [13] which limits their use in safety-critical environments. Additionally, certain common training schemes encourag... | Understanding the decision-making and trusting the reliability of Deep
Machine Learning Models is crucial for adopting such methods to safety-relevant
applications. We extend self-explainable Prototypical Variational models with
autoencoder-based out-of-distribution (OOD) detection: A Variational
Autoencoder is applied... | [
"cs.LG",
"cs.CV"
] |
# 1 INTRODUCTION
Facial micro-expression recognition (MER) is a popular task in the fields of computer vision and affective computing [1]. It has applications in wide areas such as medicine, education, and criminal investigation. Micro-expressions (MEs) are subtle and involuntary that convey genuine emotions [2], and ... | Facial micro-expression recognition (MER) is a challenging problem, due to
transient and subtle micro-expression (ME) actions. Most existing methods
depend on hand-crafted features, key frames like onset, apex, and offset
frames, or deep networks limited by small-scale and low-diversity datasets. In
this paper, we prop... | [
"cs.CV"
] |
# I. INTRODUCTION
One of the primary goals of neuromorphic computing is to emulate the structure and dynamics of biological neuronal networks, achieving both brain-like energy efficiency and high computational accuracy. This is accomplished through the use of spiking neuron models implemented on neuromorphic chips. Ov... | Spiking Neural Networks (SNNs) are computational models inspired by the
structure and dynamics of biological neuronal networks. Their event-driven
nature enables them to achieve high energy efficiency, particularly when
deployed on neuromorphic hardware platforms. Unlike conventional Artificial
Neural Networks (ANNs), ... | [
"cs.NE",
"cs.AI"
] |
# 1 Introduction
Rapid development of LLM has revolutionized the way AI and humans interact. In particular, the development of GPT (Brown et al., 2020) and introduction of ChatGPT has provided a major turning point in the field of natural language processing, spawning a new specialization called ’prompt engineering’. ... | Since the advent of large language models, prompt engineering now enables the
rapid, low-effort creation of diverse autonomous agents that are already in
widespread use. Yet this convenience raises urgent concerns about the safety,
robustness, and behavioral consistency of the underlying prompts, along with
the pressin... | [
"cs.AI",
"cs.CR"
] |
# 1 Introduction
Generative models for visual content have achieved remarkable advancements and have been applied to various fields, including amateur entertainment and professional creation. However, several challenges persist, such as the model could generate outputs that conflict with human values, harmful content,... | An effective reward model plays a pivotal role in reinforcement learning for
post-training enhancement of visual generative models. However, current
approaches of reward modeling suffer from implementation complexity due to
their reliance on extensive human-annotated preference data or meticulously
engineered quality d... | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
"# 1 Introduction\n\nThe challenge of offensive language is a continually growing concern in the fie(...TRUNCATED) | "Language detoxification involves removing toxicity from offensive language.\nWhile a neutral-toxic (...TRUNCATED) | [
"cs.CL"
] |
"# 1 Introduction\n\nMotivation. Approximate Nearest Neighbor Search (ANNS) for high-dimensional vec(...TRUNCATED) | "Approximate Nearest Neighbor Search (ANNS) presents an inherent tradeoff\nbetween performance and r(...TRUNCATED) | [
"cs.DB"
] |
"# 1. Introduction\n\nText-to-3D generation—the task of creating 3D contents from natural language(...TRUNCATED) | "Distilling pre-trained 2D diffusion models into 3D assets has driven\nremarkable advances in text-t(...TRUNCATED) | [
"cs.CV"
] |
"# 1 Introduction\n\nNegation is a fundamental and universal phenomenon found in languages worldwide(...TRUNCATED) | "Negation is a fundamental linguistic phenomenon that poses persistent\nchallenges for Large Languag(...TRUNCATED) | [
"cs.CL"
] |
"# 1 Introduction\n\nBig data is now a key focus for both government and business leaders.[3]. Howev(...TRUNCATED) | "Large Language Models (LLMs) have shown remarkable proficiency in natural\nlanguage understanding ((...TRUNCATED) | [
"cs.DB",
"cs.AI"
] |
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