pwc-archive/papers-with-abstracts huggingface dataset:
Transformer-based Planning for Symbolic Regression
Authors: Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, Chandan K. Reddy
Date: 2023-03-13 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Equation Discovery, regression, Symbolic Regression, Text Generation

Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR have demonstrated the effectiveness of pre-trained transformer-based models in generating equations as sequences, leveraging large-scale pre-training on synthetic datasets and offering notable advantages in terms of inference time over classical Genetic Programming (GP) methods. However, these models primarily rely on supervised pre-training goals borrowed from text generation and overlook equation discovery objectives like accuracy and complexity. To address this, we propose TPSR, a Transformer-based Planning strategy for Symbolic Regression that incorporates Monte Carlo Tree Search into the transformer decoding process. Unlike conventional decoding strategies, TPSR enables the integration of non-differentiable feedback, such as fitting accuracy and complexity, as external sources of knowledge into the transformer-based equation generation process. Extensive experiments on various datasets show that our approach outperforms state-of-the-art methods, enhancing the model's fitting-complexity trade-off, extrapolation abilities, and robustness to noise.
MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing
Authors: Jianfei Yang, He Huang, Yunjiao Zhou, Xinyan Chen, Yuecong Xu, Shenghai Yuan, Han Zou, Chris Xiaoxuan Lu, Lihua Xie
Date: 2023-05-12 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Action Recognition, Pose Estimation

4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals for device-free human sensing. In this paper, we propose MM-Fi, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks. MM-Fi consists of over 320k synchronized frames of five modalities from 40 human subjects. Various annotations are provided to support potential sensing tasks, e.g., human pose estimation and action recognition. Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks. We envision that MM-Fi can contribute to wireless sensing research with respect to action recognition, human pose estimation, multi-modal learning, cross-modal supervision, and interdisciplinary healthcare research.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Authors: Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, Wei Tsang Ooi
Date: 2023-10-23 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Autonomous Driving, Depth Estimation

Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously curated data, they often overlook out-of-distribution (OoD) situations. Yet, in practical settings -- especially safety-critical ones like autonomous driving -- common corruptions can arise. Addressing this oversight, we introduce a comprehensive robustness test suite, RoboDepth, encompassing 18 corruptions spanning three categories: i) weather and lighting conditions; ii) sensor failures and movement; and iii) data processing anomalies. We subsequently benchmark 42 depth estimation models across indoor and outdoor scenes to assess their resilience to these corruptions. Our findings underscore that, in the absence of a dedicated robustness evaluation framework, many leading depth estimation models may be susceptible to typical corruptions. We delve into design considerations for crafting more robust depth estimation models, touching upon pre-training, augmentation, modality, model capacity, and learning paradigms. We anticipate our benchmark will establish a foundational platform for advancing robust OoD depth estimation.
Vocabulary-free Image Classification
Authors: Alessandro Conti, Enrico Fini, Massimiliano Mancini, Paolo Rota, Yiming Wang, Elisa Ricci
Date: 2023-06-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Classification, image-classification, Image Classification, Language Modeling, Language Modelling, Semantic Similarity, Semantic Textual Similarity, Vocabulary-free Image Classification

Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories. In this work, we first empirically verify that representing this semantic space by means of an external vision-language database is the most effective way to obtain semantically relevant content for classifying the image. We then propose Category Search from External Databases (CaSED), a method that exploits a pre-trained vision-language model and an external vision-language database to address VIC in a training-free manner. CaSED first extracts a set of candidate categories from captions retrieved from the database based on their semantic similarity to the image, and then assigns to the image the best matching candidate category according to the same vision-language model. Experiments on benchmark datasets validate that CaSED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction.
TorchicTab: Semantic Table Annotation with Wikidata and Language Models
Authors: Ioannis Dasoulas, Duo Yang, Xuemin Duan, Anastasia Dimou
Date: 2023-11-20 00:00:00
Proceeding: SemTab@ISWC 2023 2023 11
Tasks: Columns Property Annotation, Column Type Annotation, Graph Matching, Table annotation

An abundance of tabular data exists and is used by a wide range of applications. However, a big portion of these data lack the semantic information necessary for users and machines to properly understand them. This lack of table semantic understanding impedes their usage in data analytics pipelines. Solutions to semantically interpret tables exist but they are focused on specific annotation tasks and types of tables, and rely on large knowledge bases, making it difficult to re-use in real-world settings. Thus, more robust systems that produce more precise annotations and adapt to different table types are needed. The Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) was introduced in an effort to benchmark semantic table interpretation systems, by evaluating them over diverse datasets and tasks. In this paper, we introduce TorchicTab, a versatile semantic table interpretation system able to annotate tables with varied structures by using either an external knowledge graph, such as Wikidata, or annotated tables with pre-defined terms for training. We evaluate our proposed system according to the different annotation tasks of the SemTab challenge. The results show that our system can produce accurate annotations for different tasks across varied datasets.
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
Authors: Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C, Benjamin Feuer, Chinmay Hegde, Ganesh Ramakrishnan, Micah Goldblum, Colin White
Date: 2023-05-04 00:00:00
Proceeding: NeurIPS 2023 11

Tabular data is one of the most commonly used types of data in machine learning. Despite recent advances in neural nets (NNs) for tabular data, there is still an active discussion on whether or not NNs generally outperform gradient-boosted decision trees (GBDTs) on tabular data, with several recent works arguing either that GBDTs consistently outperform NNs on tabular data, or vice versa. In this work, we take a step back and question the importance of this debate. To this end, we conduct the largest tabular data analysis to date, comparing 19 algorithms across 176 datasets, and we find that the 'NN vs. GBDT' debate is overemphasized: for a surprisingly high number of datasets, either the performance difference between GBDTs and NNs is negligible, or light hyperparameter tuning on a GBDT is more important than choosing between NNs and GBDTs. A remarkable exception is the recently-proposed prior-data fitted network, TabPFN: although it is effectively limited to training sets of size 3000, we find that it outperforms all other algorithms on average, even when randomly sampling 3000 training datapoints. Next, we analyze dozens of metafeatures to determine what properties of a dataset make NNs or GBDTs better-suited to perform well. For example, we find that GBDTs are much better than NNs at handling skewed or heavy-tailed feature distributions and other forms of dataset irregularities. Our insights act as a guide for practitioners to determine which techniques may work best on their dataset. Finally, with the goal of accelerating tabular data research, we release the TabZilla Benchmark Suite: a collection of the 36 'hardest' of the datasets we study. Our benchmark suite, codebase, and all raw results are available at https://github.com/naszilla/tabzilla.
Bridging RL Theory and Practice with the Effective Horizon
Authors: Cassidy Laidlaw, Stuart Russell, Anca Dragan
Date: 2023-04-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) works impressively in some environments and fails catastrophically in others. Ideally, RL theory should be able to provide an understanding of why this is, i.e. bounds predictive of practical performance. Unfortunately, current theory does not quite have this ability. We compare standard deep RL algorithms to prior sample complexity bounds by introducing a new dataset, BRIDGE. It consists of 155 deterministic MDPs from common deep RL benchmarks, along with their corresponding tabular representations, which enables us to exactly compute instance-dependent bounds. We choose to focus on deterministic environments because they share many interesting properties of stochastic environments, but are easier to analyze. Using BRIDGE, we find that prior bounds do not correlate well with when deep RL succeeds vs. fails, but discover a surprising property that does. When actions with the highest Q-values under the random policy also have the highest Q-values under the optimal policy (i.e. when it is optimal to be greedy on the random policy's Q function), deep RL tends to succeed; when they don't, deep RL tends to fail. We generalize this property into a new complexity measure of an MDP that we call the effective horizon, which roughly corresponds to how many steps of lookahead search would be needed in that MDP in order to identify the next optimal action, when leaf nodes are evaluated with random rollouts. Using BRIDGE, we show that the effective horizon-based bounds are more closely reflective of the empirical performance of PPO and DQN than prior sample complexity bounds across four metrics. We also find that, unlike existing bounds, the effective horizon can predict the effects of using reward shaping or a pre-trained exploration policy. Our code and data are available at https://github.com/cassidylaidlaw/effective-horizon
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
Authors: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, Stuart M. Shieber
Date: 2022-07-08 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Binary Classification, Language Modeling, Language Modelling, Multi-class Classification

Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Although the impact and novelty of innovations expressed in patent data are difficult to measure through traditional means, ML offers a promising set of techniques for evaluating novelty, summarizing contributions, and embedding semantics. In this paper, we introduce the Harvard USPTO Patent Dataset (HUPD), a large-scale, well-structured, and multi-purpose corpus of English-language patent applications filed to the United States Patent and Trademark Office (USPTO) between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. Unlike previously proposed patent datasets in NLP, HUPD contains the inventor-submitted versions of patent applications--not the final versions of granted patents--thereby allowing us to study patentability at the time of filing using NLP methods for the first time. It is also novel in its inclusion of rich structured metadata alongside the text of patent filings: By providing each application's metadata along with all of its text fields, the dataset enables researchers to perform new sets of NLP tasks that leverage variation in structured covariates. As a case study on the types of research HUPD makes possible, we introduce a new task to the NLP community--namely, binary classification of patent decisions. We additionally show the structured metadata provided in the dataset enables us to conduct explicit studies of concept shifts for this task. Finally, we demonstrate how HUPD can be used for three additional tasks: multi-class classification of patent subject areas, language modeling, and summarization.
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Authors: Hao-Kai Zhang, Chenghong Zhu, Mingrui Jing, Xin Wang
Date: 2023-09-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Quantum Machine Learning

Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many applications can be viewed as learning a quantum state that encodes useful data. As a quantum analog of probability distribution learning, quantum state learning is theoretically and practically essential in quantum machine learning. In this paper, we develop a no-go theorem for learning an unknown quantum state with QNNs even starting from a high-fidelity initial state. We prove that when the loss value is lower than a critical threshold, the probability of avoiding local minima vanishes exponentially with the qubit count, while only grows polynomially with the circuit depth. The curvature of local minima is concentrated to the quantum Fisher information times a loss-dependent constant, which characterizes the sensibility of the output state with respect to parameters in QNNs. These results hold for any circuit structures, initialization strategies, and work for both fixed ansatzes and adaptive methods. Extensive numerical simulations are performed to validate our theoretical results. Our findings place generic limits on good initial guesses and adaptive methods for improving the learnability and scalability of QNNs, and deepen the understanding of prior information's role in QNNs.
Bitcoin Forecasting Using ARIMA and PROPHET
Authors: Işil Yenidoğan, Aykut Çayır, Ozan Kozan, Tuğçe Dağ, Çiğdem Arslan
Date: 2018-12-09 00:00:00
Proceeding: 3rd International Conference on Computer Science and Engineering (UBMK) 2018 12
Tasks: feature selection, Model Selection, Time Series, Time Series Analysis

This paper presents all studies, methodology, and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model, the performance metrics of PROPHET and ARIMA methods are compared on the same dataset. The dataset selected for this study starts from May 2016 and ends in March 2018, which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion and feature selection. Although the time series analysis has a univariate characteristics, it is aimed to include some additional variables to each model to improve the forecasting accuracy. Those additional variables are selected based on different correlation studies between cryptocurrencies and real currencies. The model selection for both ARIMA and PROPHET is done by using threefold splitting technique considering the time series characteristics of the dataset. The threefold splitting technique gave the optimum ratios for training, validation, and test sets. Finally two different models are created and compared in terms of performance metrics. Based on the extensive testing we see that PROPHET outperforms ARIMA by 0.94 to 0.68 in R 2 values.
Increasing pedestrian detection performance through weighting of detection impairing factors
Authors: Korbinian Hagn, Oliver Grau
Date: 2022-12-08 00:00:00
Proceeding: ACM Computer Science in Cars Symposium 2022 12
Tasks: object-detection, Object Detection, Pedestrian Detection

Object detection is a matured technique, converging to the detection performance of human vision. This paper presents a method to further close the remaining gap of detection capability by investigating visual factors impairing the detectability of objects. As some of these factors are hard or impossible to measure in real sensor data, a detector is trained on synthetic data making perfect
measurements and ground truth data available at a large scale. The resulting detector is then used to calibrate an empirical weighting
loss, which weights samples of real training data and their corresponding detection impairing factors. The method is applied to the task of pedestrian detection in traffic scenes. The effectiveness of the empirical detection impairment weighting loss (DIW loss)
is demonstrated on a detector trained on the CityPersons dataset and reaches a new state-of-the-art detection performance on this
benchmark, improving the previous by 1.88%.
MMTF: Multi-Modal Temporal Fusion for Commonsense Video Question Answering
Authors: Mobeen Ahmad, Geonwoo Park, Dongchan Park, Sanguk Park
Date: 2023-10-06 00:00:00
Proceeding: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023 2023 10
Tasks: counterfactual, Question Answering, Video Question Answering

Video question answering is a challenging task that requires understanding the video and question in the same context. This becomes even harder when the questions involve reasoning, such as predicting future events or explaining counterfactual events, because they need knowledge not explicitly shown. Existing methods use coarse-grained fusion of video and language features, ignoring temporal information. To address this, we propose a novel vision-text fusion module that learns the temporal context of the video and question. Our module expands question tokens along the video's temporal axis and fuses them with video features to generate new representations with local and global context. We evaluated our method on four VideoQA datasets, including MSVD-QA, NExT-QA, Causal-VidQA, and AGQA-2.0.
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring
Authors: Shervin Mehryar, Remzi Celebi
Date: 2023-11-20 00:00:00
Proceeding: SemTab@ISWC 2023 11
Tasks: Columns Property Annotation, Column Type Annotation, Network Embedding

In this paper we investigate automated annotation of tabular data using semantic technologies in combination with neural network embedding. Specifically, we propose an anchoring model in which property and cell types from the data embedding space are aligned with ontology relation and entity types. We show that by combining the power of symbolic reasoning, neural embeddings, and loss function design, a significant performance improvement as high as 86% for column property, 82% for column type, and 87% for column qualifier annotations can be achieved based on DBpedia and Wikidata table extractions.
DREIFLUSS: A Minimalist Approach for Table Matching
Authors: Vishvapalsinhji Parmar, Alsayed Algergawy
Date: 2023-11-20 00:00:00
Proceeding: SemTab@ISWC 2023 11
Tasks: Columns Property Annotation, Column Type Annotation, Data Integration, Knowledge Graphs

This paper introduces DREIFLUSS, an innovative, minimalist approach designed to tackle the Column Type Annotation (CTA) and Column Property Annotation (CPA) tasks in the SemTab challenge. DREIFLUSS efficiently employs semantic information from well-established knowledge graphs, DBpedia, and Schema.org, to improve the annotation process. Experimental evidence illustrates the superior performance of logistic regression models trained via DREIFLUSS, resulting in precise column-type annotations and insightful relationship predictions. The findings substantiate the significance of proper sampling technique while training a model, thereby boosting the accuracy and efficiency of table matching. This research illuminates a promising pathway to enhance table matching techniques, underlining the practical ramifications of DREIFLUSS for data integration and knowledge discovery endeavors.
Exploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching
Authors: Brice Foko, Azanzi Jiomekong, Hippolyte TAPAMO, Jérémy Buisson, Sanju Tiwari
Date: 2023-11-20 00:00:00
Proceeding: SemTab@ISWC 2023 11
Tasks: Cell Entity Annotation, Columns Property Annotation, Column Type Annotation, Graph Matching, Knowledge Graphs

The present research investigates the use of Naive Bayes classifiers to match knowledge graphs and tabular data, with particular emphasis on Column Type Annotation, Cell Entity Annotation, Column Property Annotation and Table Topic Detection. Using feature extraction techniques such as number of co-occurrences and term frequency, the study evaluates the effectiveness and performance of Naive Bayes classifiers on a variety of datasets. The proposed method is straightforward and generic, making a contribution to the field of knowledge graph matching and demonstrating the potential of Naive Bayes classifiers for the integration and interoperability of tabular data and knowledge graphs.
Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers
Authors: P. Rodriguez-Fernandez, N. T. Howard, A. Saltzman, S. Kantamneni, J. Candy, C. Holland, M. Balandat, S. Ament, A. E. White
Date: 2023-12-19 00:00:00
Tasks: GPU, Prediction

This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.