136 research outputs found
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and
XLM, have achieved great success in cross-lingual representation learning.
However, when applied to zero-shot cross-lingual transfer tasks, most existing
methods use only single-language input for LM finetuning, without leveraging
the intrinsic cross-lingual alignment between different languages that proves
essential for multilingual tasks. In this paper, we propose FILTER, an enhanced
fusion method that takes cross-lingual data as input for XLM finetuning.
Specifically, FILTER first encodes text input in the source language and its
translation in the target language independently in the shallow layers, then
performs cross-language fusion to extract multilingual knowledge in the
intermediate layers, and finally performs further language-specific encoding.
During inference, the model makes predictions based on the text input in the
target language and its translation in the source language. For simple tasks
such as classification, translated text in the target language shares the same
label as the source language. However, this shared label becomes less accurate
or even unavailable for more complex tasks such as question answering, NER and
POS tagging. To tackle this issue, we further propose an additional
KL-divergence self-teaching loss for model training, based on auto-generated
soft pseudo-labels for translated text in the target language. Extensive
experiments demonstrate that FILTER achieves new state of the art on two
challenging multilingual multi-task benchmarks, XTREME and XGLUE.Comment: Accepted to AAAI 2021; Top-1 Performance on XTREME
(https://sites.research.google/xtreme, September 8, 2020) and XGLUE
(https://microsoft.github.io/XGLUE, September 14, 2020) benchmar
Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition
The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition
Fuzzy superpixels for polarimetric SAR images classification
Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a speciļ¬c application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classiļ¬cation. However, no superpixel algorithm is especially designed for image classiļ¬cation. It is believed that both mixed superpixels and pure superpixels exist in an image.Nevertheless, mixed superpixels have negative effects on classiļ¬cation accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classiļ¬cation. In this paper, ļ¬rst, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels.In fuzzy superpixels ,not al lpixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second,a new algorithm, named FuzzyS(FS),is proposed to generate fuzzy superpixels for PolSAR image classiļ¬cation. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms
Research on the Mechanism of Entrepreneurship Education on College Studentsā Entrepreneurial Willingness and Its Future Prediction
The strength of college studentsā entrepreneurial willingness is a barometer for measuring the effectiveness of entrepreneurship education. It is also an important avenue for college students to expand their employment opportunities and enhance the quality of their employment in the face of new employment trends. Comprehensive universities offer a wide range of disciplines and great professional specialization. It is of great significance to explore the diversity results in college studentsā entrepreneurship education indicators. According to the data on the relationship between entrepreneurial education and entrepreneurship willingness in comprehensive universities in Jiangsu province, various factors such as subject characteristics, work experience, educational background, and family environment significantly impact college studentsā willingness to become entrepreneurs. The implementation of entrepreneurship education, including the awakening of entrepreneurial consciousness, the cultivation of entrepreneurial abilities, and the improvement of entrepreneurial willingness, has a direct impact on college studentsā willingness to start their own businesses
Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning
How neural networks in the human brain represent commonsense knowledge, and
complete related reasoning tasks is an important research topic in
neuroscience, cognitive science, psychology, and artificial intelligence.
Although the traditional artificial neural network using fixed-length vectors
to represent symbols has gained good performance in some specific tasks, it is
still a black box that lacks interpretability, far from how humans perceive the
world. Inspired by the grandmother-cell hypothesis in neuroscience, this work
investigates how population encoding and spiking timing-dependent plasticity
(STDP) mechanisms can be integrated into the learning of spiking neural
networks, and how a population of neurons can represent a symbol via guiding
the completion of sequential firing between different neuron populations. The
neuron populations of different communities together constitute the entire
commonsense knowledge graph, forming a giant graph spiking neural network.
Moreover, we introduced the Reward-modulated spiking timing-dependent
plasticity (R-STDP) mechanism to simulate the biological reinforcement learning
process and completed the related reasoning tasks accordingly, achieving
comparable accuracy and faster convergence speed than the graph convolutional
artificial neural networks. For the fields of neuroscience and cognitive
science, the work in this paper provided the foundation of computational
modeling for further exploration of the way the human brain represents
commonsense knowledge. For the field of artificial intelligence, this paper
indicated the exploration direction for realizing a more robust and
interpretable neural network by constructing a commonsense knowledge
representation and reasoning spiking neural networks with solid biological
plausibility
Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification
Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels
Evaluating Very Long-Term Conversational Memory of LLM Agents
Existing works on long-term open-domain dialogues focus on evaluating model
responses within contexts spanning no more than five chat sessions. Despite
advancements in long-context large language models (LLMs) and retrieval
augmented generation (RAG) techniques, their efficacy in very long-term
dialogues remains unexplored. To address this research gap, we introduce a
machine-human pipeline to generate high-quality, very long-term dialogues by
leveraging LLM-based agent architectures and grounding their dialogues on
personas and temporal event graphs. Moreover, we equip each agent with the
capability of sharing and reacting to images. The generated conversations are
verified and edited by human annotators for long-range consistency and
grounding to the event graphs. Using this pipeline, we collect LoCoMo, a
dataset of very long-term conversations, each encompassing 300 turns and 9K
tokens on avg., over up to 35 sessions. Based on LoCoMo, we present a
comprehensive evaluation benchmark to measure long-term memory in models,
encompassing question answering, event summarization, and multi-modal dialogue
generation tasks. Our experimental results indicate that LLMs exhibit
challenges in understanding lengthy conversations and comprehending long-range
temporal and causal dynamics within dialogues. Employing strategies like
long-context LLMs or RAG can offer improvements but these models still
substantially lag behind human performance.Comment: 19 pages; Project page: https://snap-research.github.io/locomo
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