OpenRaiser/Intern-Atlas huggingface dataset:
A Generic Learning Framework for Sequential Recommendation with Distribution Shifts
Authors: Zhengyi Yang, Xiangnan He, Jizhi Zhang, Jiancan Wu, Xin Xin, Jiawei Chen, Xiang Wang
Venue: Conference SIGIR 2023
Year: 2023

Leading sequential recommendation (SeqRec) models adopt empirical risk minimization (ERM) as the learning framework, which inherently assumes that the training data (historical interaction sequences) and the testing data (future interactions) are drawn from the same distribution. However, such i.i.d. assumption hardly holds in practice, due to the online serving and dynamic nature of recommender system.For example, with the streaming of new data, the item popularity distribution would change, and the user preference would evolve after consuming some items. Such distribution shifts could undermine the ERM framework, hurting the model's generalization ability for future online serving. In this work, we aim to develop a generic learning framework to enhance the generalization of recommenders in the dynamic environment. Specifically, on top of ERM, we devise a Distributionally Robust Optimization mechanism for SeqRec (DROS). At its core is our carefully-designed distribution adaption paradigm, which considers the dynamics of data distribution and explores possible distribution shifts between training and testing. Through this way, we can endow the backbone recommenders with better generalization ability.It is worth mentioning that DROS is an effective model-agnostic learning framework, which is applicable to general recommendation scenarios.Theoretical analyses show that DROS enables the backbone recommenders to achieve robust performance in future testing data.Empirical studies verify the effectiveness against dynamic distribution shifts of DROS. Codes are anonymously open-sourced at https://github.com/YangZhengyi98/DROS.
Can llms reason like humans? assessing theory of mind reasoning in llms for open-ended questions
Authors: Maryam Amirizaniani, Elias Martin, Maryna Sivachenko, Afra Mashhadi, Chirag Shah
Venue: Conference CIKM 2024
Year: 2024

Theory of mind (ToM) reasoning involves understanding that others have intentions, emotions, and thoughts, which is crucial for regulating one's reasoning. Although large language models (LLMs) excel in tasks such as summarization, question answering, and translation, they still face challenges with ToM reasoning, especially in open-ended questions. Despite advancements, the extent to which LLMs truly understand ToM reasoning and how closely it aligns with human ToM reasoning remains inadequately explored in open-ended scenarios. Motivated by this gap, we assess the abilities of LLMs to perceive and integrate human intentions and emotions into their ToM reasoning processes within open-ended questions. Our study utilizes posts from Reddit's ChangeMyView platform, which demands nuanced social reasoning to craft persuasive responses. Our analysis, comparing semantic similarity and lexical overlap metrics between responses generated by humans and LLMs, reveals clear disparities in ToM reasoning capabilities in open-ended questions, with even the most advanced models showing notable limitations. To enhance LLM capabilities, we implement a prompt tuning method that incorporates human intentions and emotions, resulting in improvements in ToM reasoning performance. However, despite these improvements, the enhancement still falls short of fully achieving human-like reasoning. This research highlights the deficiencies in LLMs' social reasoning and demonstrates how integrating human intentions and emotions can boost their effectiveness.
Crowdpt: Summarizing crowd opinions as professional analyst
Authors: Chung-Chi Chen, Hen-Hsen Huang, Chia-Wen Tsai, Hsin-Hsi Chen
Venue: Conference WWW 2019
Year: 2019

This paper demonstrates a novel analytics service, CrowdPT, for capturing the key information, price target (PT), of individual investors on social media. PT, which is mentioned as a conclusion in most of analysts' reports, indicates not only the market sentiment (bullish/bearish) of investors, but also the analysis results. In order to provide the latest opinions of individual investors, we monitor Twitter in real time and update the information in price chart daily. For all component stocks in Dow Jones Industrial Average, textual information from numerous tweets is summarized into a single number, PT, in CrowdPT. Case studies confirm the effectiveness of our analytics service in the financial domain, and show that capturing the PT of individual investors is promising for stock price prediction. The Web API of CrowdPT is also provided for academic purpose.
Machine learning in finance
Authors: Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, José A. Rodríguez-Serrano, Tanveer A. Faruquie, Saurabh Nagrecha
Venue: Conference KDD 2021
Year: 2021

The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There are also newly emerging threats such as fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all these problems including semi-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions. We have invited regular papers, positional papers and extended abstracts of work in progress. We have also encouraged short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. This event is the fourth in a sequence of finance related workshops we have organized at KDD since 2017.
Bias-reduced uncertainty estimation for deep neural classifiers
Authors: Yonatan Geifman, Guy Uziel, Ran El-Yaniv
Venue: Conference ICLR 2019
Year: 2019

We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand. We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident. We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting. Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification). We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.
Large language models for tabular data: Progresses and future directions
Authors: Haoyu Dong, Zhiruo Wang
Venue: Conference SIGIR 2024
Year: 2024

Tables contain a significant portion of the world's structured information. The ability to efficiently and accurately understand, process, reason about, analyze, and generate tabular data is critical for achieving Artificial General Intelligence (AGI) systems. However, despite their prevalence and importance, tables present unique challenges due to their structured nature and the diverse semantics embedded within them. Textual content, numerical values, visual formats, and even formulas in tables carry rich semantic information that is often underutilized due to the complexity of accurately interpreting and integrating. Fortunately, the advent of Large Language Models (LLMs) has opened new frontiers in natural language processing (NLP) and machine learning (ML), showing remarkable success in understanding and generating text, code, etc. Applying these advanced models to the domain of tabular data holds the promise of significant breakthroughs in how we process and leverage structured information. Therefore, this tutorial aims to provide a comprehensive study of the advances, challenges, and opportunities in leveraging cutting-edge LLMs for tabular data. By introducing methods of prompting or training cutting-edge LLMs for table interpreting, processing, reasoning, analytics, and generation, we aim to equip researchers and practitioners with the knowledge and tools needed to unlock the full potential of LLMs for tabular data in their domains.
Summarizing and exploring tabular data in conversational search
Authors: Shuo Zhang 0006, Zhuyun Dai, Krisztian Balog, Jamie Callan
Venue: Conference SIGIR 2020
Year: 2020

Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset. It includes annotated table summaries, which not only answer questions but also help people explore other information in the table. We utilize this dataset to develop automatic table summarization systems as SOTA baselines. Based on the experimental results, we identify challenges and point out future research directions that this resource will support.
Learning the semantics of words and pictures
Authors: Kobus Barnard, David A. Forsyth
Venue: Conference ICCV 2001
Year: 2001

We present a statistical model for organizing image collections which integrates semantic information provided by associate text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on text and/or image features. Furthermore, since the model learns relationships between text and image features, it can be used for novel applications such as associating words with pictures, and unsupervised learning for object recognition.
Offline Tracing and Representation of Signatures
Authors: Jack Chien-Jan Pan, Sukhan Lee 0001
Venue: Conference CVPR 1991
Year: 1991

An approach to the representation of signatures in an offline environment that makes a tracing of a signature in a manner similar to that of a human normally does, and incorporates the dynamic information of the tracing sequence into the representation of the signature, is presented. Tracing involves hierarchical decision-making for stroke identification and ordering based on a set of heuristic rules. These heuristic rules work as operators that transform a two-dimensional (2-D) spatial pattern into a one-dimensional (1-D) temporal pattern, thus making it possible to extract the dynamic features of a signature. Following the stroke sequence of a signature identified by the tracing, a multiresolution critical-point segmentation method is proposed to extract local feature points, at varying degrees of scale and coarseness, for representation. To make the representation translation-, rotation-, and scaling-invariant, a critical-point normalization method is introduced. Experimental results are shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
An Image­ based Bayesian Framework for Face Detection
Authors: Lingmin Meng, Truong Q. Nguyen, David A. Castañón
Venue: Conference CVPR 2000
Year: 2000

In this paper, we present a novel approach for frontal face detection in gray-scale images. We represent both faces and clutter by using two-dimensional wavelet decomposition. To characterize the statistical dependency between different levels of wavelet, we introduce a Hidden Markov Model (HMM), in which a number of discrete states at each level capture the diversity of faces as well as clutter. Our experiments indicate that the proposed algorithm outperforms conventional template-based methods such as matched filter and eigenface methods.
Fast Face Detection Using Subspace Discriminant Wavelet Features
Authors: Ying Zhu 0006, Stuart C. Schwartz, Michael T. Orchard
Venue: Conference CVPR 2000
Year: 2000

Computation complexity is an important issue for current face detection systems. This paper proposes a subspace approach to capture local discriminative features in the space-frequency domain for fast face detection. Based on orthonormal wavelet packet analysis, we develop a discriminant subspace algorithm to search for the "minimum cost" subspace of the high-dimensional signal space, which leads to a set of wavelet features with maximum class discrimination and dimensionality reduction. Detailed (high frequency) information within local facial areas shows noticeable discrimination ability for face detection problem. We demonstrate the algorithm in the context of detecting frontal view faces in a complex background. Discrete pattern distribution functions and fast likelihood ratio detection are adopted by the system. Because of the reduced dimensionality, feature discrimination and the discrete stochastic model, our face detection system consumes much less computation while the performance is comparable with other reported leading systems.
Probabilistic latent semantic indexing
Authors: Thomas Hofmann
Venue: Conference SIGIR 1999
Year: 1999

Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain speci c synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing LSI by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and de nes a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching metho d s a s w ell as over LSI. In particular, the combination of models with di erent dimensionalities has proven to be advantageous.
Retrieving spoken documents by combining multiple index sources
Authors: Gareth J. F. Jones, J. T. Foote, Karen Sparck Jones, Steve J. Young
Venue: Conference SIGIR 1996
Year: 1996

This paper presents domain-independent methods of spoken document retrieval. Both a continuous-speech large vocabulary recognition system, and a phone-lattice word spotter,
Indexing for topics in videos using foils
Authors: Tanveer Fathima Syeda-Mahmood
Venue: Conference CVPR 2000
Year: 2000

A long-standing goal of distance learning has been to provide a quality of learning comparable to the face-to-face environment of a traditional classroom for teaching or training. One of the fundamental problems in achieving this goal is providing effective ways of high-level semantic querying such as for the retrieval of relevant learning material relating to a topic of discussion. In this paper we present a method of identifying video segments relating to a topic of discussion by indexing videos using the image and text content of foils. Specifically, we present a novel method of locating and recognizing foil images in video using the color and spatial layout geometry of their regions. We then search the audio associated with video based on the text content of the foil to identify related video segments in which concepts represented on a foil are heard. Finally, we combine the results of foil image and text search of video exploiting their time co-occurrence. The resulting identification of topics is evaluated in the domain of classroom lectures and talks.
Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection
Authors: Alper Yilmaz 0001
Venue: Conference CVPR 2007
Year: 2007

Tracking objects using the mean shift method is performed by iteratively translating a kernel in the image space such that the past and current object observations are similar. Traditional mean shift method requires a symmetric kernel, such as a circle or an ellipse, and assumes constancy of the object scale and orientation during the course of tracking. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we present an object tracking method based on the asymmetric kernel mean shift, in which the scale and orientation of the kernel adaptively change depending on the observations at each iteration. Proposed method extends the traditional mean shift tracking, which is performed in the image coordinates, by including the scale and orientation as additional dimensions and simultaneously estimates all the unknowns in a few number of mean shift iterations. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not effected by the scale and orientation changes of the tracked objects; 3) it is less prone to the background clutter.
3d human pose from silhouettes by relevance vector regression
Authors: Ankur Agarwal, Bill Triggs
Venue: Conference CVPR 2004
Year: 2004

We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body pans in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. For the main regression, we evaluate both regularized least squares and relevance vector machine (RVM) regressors over both linear and kernel bases. The RVM's provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. For realism and good generalization with respect to viewpoints, we train the regressors on images resynthesized from real human motion capture data, and test it both quantitatively on similar independent test data, and qualitatively on a real image sequence. Mean angular errors of 6-7 degrees are obtained - a factor of 3 better than the current state of the art for the much simpler upper body problem.
Estimating 3d body pose using uncalibrated cameras
Authors: Rómer Rosales, Matheen Siddiqui, Jonathan Alon, Stan Sclaroff
Venue: Conference CVPR 2001
Year: 2001

An approach for estimating 3D body pose from multiple, uncalibrated views is proposed. First, a mapping from image features to 2D body joint locations is computed using a statistical framework that yields a set of several body pose hypotheses. The concept of a "virtual camera" is introduced that makes this mapping invariant to translation, image-plane rotation, and scaling of the input. As a consequence, the calibration matrices (intrinsics) of the virtual cameras can be considered completely known, and their poses are known up to a single angular displacement parameter Given pose hypotheses obtained in the multiple virtual camera views, the recovery of 3D body pose and camera relative orientations is formulated as a stochastic optimization problem. An Expectation-Maximization algorithm is derived that can obtain the locally most likely (self-consistent) combination of body pose hypotheses. Performance of the approach is evaluated with synthetic sequences as well as real video sequences of human motion.
Stereo Correspondence by Dynamic Programming on a Tree
Authors: Olga Veksler
Venue: Conference CVPR 2005
Year: 2005

Dynamic programming on a scanline is one of the oldest and still popular methods for stereo correspondence. While efficient, its performance is far from the state of the art because the vertical consistency between the scanlines is not enforced. We re-examine the use of dynamic programming for stereo correspondence by applying it to a tree structure, as opposed to the individual scanlines. The nodes of this tree are all the image pixels, but only the "most important" edges of the 4 connected neighbourhood system are included. Thus our algorithm is truly a global optimization method because disparity estimate at one pixel depends on the disparity estimates at all the other pixels, unlike the scanline based methods. We evaluate our algorithm on the benchmark Middlebury database. The algorithm is very fast; it takes only a fraction of a second for a typical image. The results are considerably better than that of the scanline based methods. While the results are not the state of the art, our algorithm offers a good trade off in terms of accuracy and computational efficiency.
CHoG: Compressed histogram of gradients a low bit-rate feature descriptor
Authors: Vijay Chandrasekhar 0001, Gabriel Takacs, David M. Chen, Sam S. Tsai, Radek Grzeszczuk, Bernd Girod
Venue: Conference CVPR 2009
Year: 2009

Establishing visual correspondences is an essential component of many computer vision problems, and is often done with robust, local feature-descriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile distributed camera networks and large indexing problems. We propose a framework for computing low bit-rate feature descriptors with a 20× reduction in bit rate. The framework is low complexity and has significant speed-up in the matching stage. We represent gradient histograms as tree structures which can be efficiently compressed. We show how to efficiently compute distances between descriptors in their compressed representation eliminating the need for decoding. We perform a comprehensive performance comparison with SIFT, SURF, and other low bit-rate descriptors and show that our proposed CHoG descriptor outperforms existing schemes.
Fast visibility restoration from a single color or gray level image
Authors: Jean-Philippe Tarel, Nicolas Hautière
Venue: Conference ICCV 2009
Year: 2009

One source of difficulties when processing outdoor images is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images or gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quantitative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach.