OpenRaiser/Intern-Atlas huggingface dataset:
Curriculum Learning for Dense Retrieval Distillation
Authors: Hansi Zeng, Hamed Zamani, Vishwa Vinay
Venue: Conference SIGIR 2022
Year: 2022

Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a generic curriculum learning based optimization framework called CL-DRD that controls the difficulty level of training data produced by the re-ranking (teacher) model. CL-DRD iteratively optimizes the dense retrieval (student) model by increasing the difficulty of the knowledge distillation data made available to it. In more detail, we initially provide the student model coarse-grained preference pairs between documents in the teacher's ranking, and progressively move towards finer-grained pairwise document ordering requirements. In our experiments, we apply a simple implementation of the CL-DRD framework to enhance two state-of-the-art dense retrieval models. Experiments on three public passage retrieval datasets demonstrate the effectiveness of our proposed framework.
Leapattack: Hard-label adversarial attack on text via gradient-based optimization
Authors: Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang 0006, Fenglong Ma
Venue: Conference KDD 2022
Year: 2022

Generating text adversarial examples in the hard-label setting is a more realistic and challenging black-box adversarial attack problem, whose challenge comes from the fact that gradient cannot be directly calculated from discrete word replacements. Consequently, the effectiveness of gradient-based methods for this problem still awaits improvement. In this paper, we propose a gradient-based optimization method named LeapAttack to craft high-quality text adversarial examples in the hard-label setting. To specify, LeapAttack employs the word embedding space to characterize the semantic deviation between the two words of each perturbed substitution by their difference vector. Facilitated by this expression, LeapAttack gradually updates the perturbation direction and constructs adversarial examples in an iterative round trip: firstly, the gradient is estimated by transforming randomly sampled word candidates to continuous difference vectors after moving the current adversarial example near the decision boundary; secondly, the estimated gradient is mapped back to a new substitution word based on the cosine similarity metric. Extensive experimental results show that in the general case LeapAttack can efficiently generate high-quality text adversarial examples with the highest semantic similarity and the lowest perturbation rate in the hard-label setting.
Multi-scale structural saliency for signature detection
Authors: Guangyu Zhu 0004, Yefeng Zheng 0001, David S. Doermann, Stefan Jaeger 0001
Venue: Conference CVPR 2007
Year: 2007

Detecting and segmenting free-form objects from cluttered backgrounds is a challenging problem in computer vision. Signature detection in document images is one classic example and as of yet no reasonable solutions have been presented. In this paper, we propose a novel multi-scale approach to jointly detecting and segmenting signatures from documents with diverse layouts and complex backgrounds. Rather than focusing on local features that typically have large variations, our approach aims to capture the structural saliency of a signature by searching over multiple scales. This detection framework is general and computationally tractable. We present a saliency measure based on a signature production model that effectively quantifies the dynamic curvature of 2-D contour fragments. Our evaluation using large real world collections of handwritten and machine printed documents demonstrates the effectiveness of this joint detection and segmentation approach.
On the local optimality of lambdarank
Authors: Pinar Donmez, Krysta M. Svore, Christopher J. C. Burges
Venue: Conference SIGIR 2009
Year: 2009

A machine learning approach to learning to rank trains a model to optimize a target evaluation measure with repect to training data. Currently, existing information retrieval measures are impossible to optimize directly except for models with a very small number of parameters. The IR community thus faces a major challenge: how to optimize IR measures of interest directly. In this paper, we present a solution. Specifically, we show that LambdaRank, which smoothly approximates the gradient of the target measure, can be adapted to work with four popular IR target evaluation measures using the same underlying gradient construction. It is likely, therefore, that this construction is extendable to other evaluation measures. We empirically show that LambdaRank finds a locally optimal solution for mean [email protected], mean NDCG, MAP and MRR with a 99% confidence rate. We also show that the amount of effective training data varies with IR measure and that with a sufficiently large training set size, matching the training optimization measure to the target evaluation measure yields the best accuracy.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Authors: Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
Venue: Conference ICLR 2019
Year: 2019

Sorting input objects is an important step in many machine learning pipelines. However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization. In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting operator from permutation matrices to the set of unimodal row-stochastic matrices, where every row sums to one and has a distinct arg max. This relaxation permits straight-through optimization of any computational graph involve a sorting operation. Further, we use this relaxation to enable gradient-based stochastic optimization over the combinatorially large space of permutations by deriving a reparameterized gradient estimator for the Plackett-Luce family of distributions over permutations. We demonstrate the usefulness of our framework on three tasks that require learning semantic orderings of high-dimensional objects, including a fully differentiable, parameterized extension of the k-nearest neighbors algorithm.
Wikipedia reader navigation: When synthetic data is enough
Authors: Akhil Arora, Martin Gerlach, Tiziano Piccardi, Alberto García-Durán, Robert West
Venue: Conference WSDM 2022
Year: 2022

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.
Automatically suggesting topics for augmenting text documents
Authors: Robert West, Doina Precup, Joelle Pineau
Venue: Conference CIKM 2010
Year: 2010

We present a method for automated topic suggestion. Given a plain-text input document, our algorithm produces a ranking of novel topics that could enrich the input document in a meaningful way. It can thus be used to assist human authors, who often fail to identify important topics relevant to the context of the documents they are writing. Our approach marries two algorithms originally designed for linking documents to Wikipedia articles, proposed by Milne and Witten [15] and West et al. [22]. While neither of them can suggest novel topics by itself, their combination does have this capability. The key step towards finding missing topics consists in generalizing from a large background corpus using principal component analysis. In a quantitative evaluation we conclude that our method achieves the precision of human editors when input documents are Wikipedia articles, and we complement this result with a qualitative analysis showing that the approach also works well on other types of input documents.
The intriguing role of module criticality in the generalization of deep networks
Authors: Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi
Venue: Conference ICLR 2020
Year: 2020

We study the phenomenon that some modules of deep neural networks (DNNs) are more \emph{critical} than others. Meaning that rewinding their parameter values back to initialization, while keeping other modules fixed at the trained parameters, results in a large drop in the network's performance. Our analysis reveals interesting properties of the loss landscape which leads us to propose a complexity measure, called {\em module criticality}, based on the shape of the valleys that connects the initial and final values of the module parameters. We formulate how generalization relates to the module criticality, and show that this measure is able to explain the superior generalization performance of some architectures over others, whereas earlier measures fail to do so.
FUNU: boosting machine unlearning efficiency by filtering unnecessary unlearning
Authors: Zitong Li, Qingqing Ye, Haibo Hu
Venue: Conference WWW 2025
Year: 2025

Machine unlearning is an emerging field that selectively removes specific data samples from a trained model. This capability is crucial for addressing privacy concerns, complying with data protection regulations, and correcting errors or biases introduced by certain data. Unlike traditional machine learning, where models are typically static once trained, machine unlearning facilitates dynamic updates that enable the model to ''forget'' information without requiring complete retraining from scratch. There are various machine unlearning methods, some of which are more time-efficient when data removal requests are fewer. To decrease the execution time of such machine unlearning methods, we aim to reduce the size of data removal requests based on the fundamental assumption that the removal of certain data would not result in a distinguishable retrained model. We first propose the concept of unnecessary unlearning, which indicates that the model would not alter noticeably after removing some data points. Subsequently, we review existing solutions that can be used to solve our problem. We highlight their limitations in adaptability to different unlearning scenarios and their reliance on manually selected parameters. We consequently put forward FUNU, a method to identify data points that lead to unnecessary unlearning. FUNU circumvents the limitations of existing solutions. The idea is to discover data points within the removal requests that have similar neighbors in the remaining dataset. We utilize a reference model to set parameters for finding neighbors, inspired from the area of model memorization. We provide a theoretical analysis of the privacy guarantee offered by FUNU and conduct extensive experiments to validate its efficacy.
Fedhlt: Efficient federated low-rank adaption with hierarchical language tree for multilingual modeling
Authors: Zhihan Guo, Yifei Zhang, Zhuo Zhang, Zenglin Xu, Irwin King
Venue: Conference WWW 2024
Year: 2024

Federated Multilingual Modeling (FMM) has become an essential approach in natural language processing (NLP) due to increasing linguistic diversity and the heightened emphasis on data privacy. However, FMM faces two primary challenges: 1) the high communication costs inherent in network operations, and 2) the complexities arising from parameter interference, as languages exhibit both unique characteristics and shared features. To tackle these issues, we introduce a communication-efficient framework for Multilingual Modeling (MM) that combines low-rank adaptation with a hierarchical language tree structure. Our method maintains the base model's weights while focusing on updating only the Low-rank adaptation (LoRA) parameters, significantly reducing communication costs. Additionally, we mitigate parameter conflicts by organizing languages based on their familial ties rather than merging all LoRA parameters together. Our experimental findings reveal that this novel model surpasses established baseline models in performance and markedly decreases communication overhead.
Equivariance versus augmentation for spherical images
Authors: Jan E. Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson
Venue: Conference ICML 2022
Year: 2022

We analyze the role of rotational equivariance in convolutional neural networks (CNNs) applied to spherical images. We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation. The chosen architectures can be considered baseline references for the respective design paradigms. Our models are trained and evaluated on single or multiple items from the MNIST or FashionMNIST dataset projected onto the sphere. For the task of image classification, which is inherently rotationally invariant, we find that by considerably increasing the amount of data augmentation and the size of the networks, it is possible for the standard CNNs to reach at least the same performance as the equivariant network. In contrast, for the inherently equivariant task of semantic segmentation, the non-equivariant networks are consistently outperformed by the equivariant networks with significantly fewer parameters. We also analyze and compare the inference latency and training times of the different networks, enabling detailed tradeoff considerations between equivariant architectures and data augmentation for practical problems. The equivariant spherical networks used in the experiments are available at https://github.com/JanEGerken/sem_seg_s2cnn .
Improving medical code prediction from clinical text via incorporating online knowledge sources
Authors: Tian Bai, Slobodan Vucetic
Venue: Conference WWW 2019
Year: 2019

Clinical notes contain detailed information about health status of patients for each of their encounters with a health system. Developing effective models to automatically assign medical codes to clinical notes has been a long-standing active research area. Despite a great recent progress in medical informatics fueled by deep learning, it is still a challenge to find the specific piece of evidence in a clinical note which justifies a particular medical code out of all possible codes. Considering the large amount of online disease knowledge sources, which contain detailed information about signs and symptoms of different diseases, their risk factors, and epidemiology, there is an opportunity to exploit such sources. In this paper we consider Wikipedia as an external knowledge source and propose Knowledge Source Integration (KSI), a novel end-to-end code assignment framework, which can integrate external knowledge during training of any baseline deep learning model. The main idea of KSI is to calculate matching scores between a clinical note and disease related Wikipedia documents, and combine the scores with output of the baseline model. To evaluate KSI, we experimented with automatic assignment of ICD-9 diagnosis codes to the emergency department clinical notes from MIMIC-III data set, aided by Wikipedia documents corresponding to the ICD-9 codes. We evaluated several baseline models, ranging from logistic regression to recently proposed deep learning models known to achieve the state-of-the-art accuracy on clinical notes. The results show that KSI consistently improves the baseline models and that it is particularly successful in assignment of rare codes. In addition, by analyzing weights of KSI models, we can gain understanding about which words in Wikipedia documents provide useful information for predictions.
Interpretable representation learning for healthcare via capturing disease progression through time
Authors: Tian Bai, Shanshan Zhang, Brian L. Egleston, Slobodan Vucetic
Venue: Conference KDD 2018
Year: 2018

Various deep learning models have recently been applied to predictive modeling of Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR data, each patient is represented as a sequence of temporally ordered irregularly sampled visits to health providers, where each visit is recorded as an unordered set of medical codes specifying patient's diagnosis and treatment provided during the visit. Based on the observation that different patient conditions have different temporal progression patterns, in this paper we propose a novel interpretable deep learning model, called Timeline. The main novelty of Timeline is that it has a mechanism that learns time decay factors for every medical code. This allows the Timeline to learn that chronic conditions have a longer lasting impact on future visits than acute conditions. Timeline also has an attention mechanism that improves vector embeddings of visits. By analyzing the attention weights and disease progression functions of Timeline, it is possible to interpret the predictions and understand how risks of future visits change over time. We evaluated Timeline on two large-scale real world data sets. The specific task was to predict what is the primary diagnosis category for the next hospital visit given previous visits. Our results show that Timeline has higher accuracy than the state of the art deep learning models based on RNN. In addition, we demonstrate that time decay factors and attentions learned by Timeline are in accord with the medical knowledge and that Timeline can provide a useful insight into its predictions.
Podtile: Facilitating podcast episode browsing with auto-generated chapters
Authors: Azin Ghazimatin, Ekaterina Garmash, Gustavo Penha, Kristen Sheets, Martin Achenbach, Oguz Semerci, Remi Galvez, Marcus Tannenberg, Sahitya Mantravadi, Divya Narayanan, Ofeliya Kalaydzhyan, Douglas Cole, Ben Carterette, Ann Clifton, Paul N. Bennett, Claudia Hauff, Mounia Lalmas
Venue: Conference CIKM 2024
Year: 2024

Listeners of long-form talk-audio content, such as podcast episodes, often find it challenging to understand the overall structure and locate relevant sections. A practical solution is to divide episodes into chapters'semantically coherent segments labeled with titles and timestamps. Since most episodes on our platform at Spotify currently lack creator-provided chapters, automating the creation of chapters is essential. Scaling the chapterization of podcast episodes presents unique challenges. First, episodes tend to be less structured than written texts, featuring spontaneous discussions with nuanced transitions. Second, the transcripts are usually lengthy, averaging about 16,000 tokens, which necessitates efficient processing that can preserve context. To address these challenges, we introduce PODTILE, a fine-tuned encoder-decoder transformer to segment conversational data. The model simultaneously generates chapter transitions and titles for the input transcript. To preserve context, each input text is augmented with global context, including the episode's title, description, and previous chapter titles. In our intrinsic evaluation, PODTILE achieved a 11% improvement in ROUGE score over the strongest previous baseline. Additionally, we provide insights into the practical benefits of auto-generated chapters for listeners navigating episode content. Our findings indicate that auto-generated chapters serve as a useful tool for engaging with less popular podcasts. Finally, we present empirical evidence that using chapter titles can enhance the effectiveness of sparse retrieval in search tasks.
Outline generation: Understanding the inherent content structure of documents
Authors: Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng
Venue: Conference SIGIR 2019
Year: 2019

In this paper, we introduce and tackle the Outline Generation (OG) task, which aims to unveil the inherent content structure of a multi-paragraph document by identifying its potential sections and generating the corresponding section headings. Without loss of generality, the OG task can be viewed as a novel structured summarization task. To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings. The first one is the foundation for section identification, while the latter two are critical for consistent heading generation. In this work, we formulate the OG task as a hierarchical structured prediction problem, i.e., to first predict a sequence of section boundaries and then a sequence of section headings accordingly. We propose a novel hierarchical structured neural generation model, named HiStGen, for the task. Our model attempts to capture the three-level coherence via the following ways. First, we introduce a Markov paragraph dependency mechanism between context paragraphs for section identification. Second, we employ a section-aware attention mechanism to ensure the semantic coherence between a section and its heading. Finally, we leverage a Markov heading dependency mechanism and a review mechanism between context headings to improve the consistency and eliminate duplication between section headings. Besides, we build a novel Wriptsize IKI OG dataset, a public collection which consists of over 1.75 million document-outline pairs for research on the OG task. Experimental results on our benchmark dataset demonstrate that our model can significantly outperform several state-of-the-art sequential generation models for the OG task.