pwc-archive/papers-with-abstracts huggingface dataset:
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
Authors: Xiong-Hui Chen, Junyin Ye, Hang Zhao, Yi-Chen Li, Haoran Shi, Yu-Yan Xu, Zhihao Ye, Si-Hang Yang, Anqi Huang, Kai Xu, Zongzhang Zhang, Yang Yu
Date: 2023-10-09 00:00:00
Tasks: Imitation Learning

Imitation learning (IL) enables agents to mimic expert behaviors. Most previous IL techniques focus on precisely imitating one policy through mass demonstrations. However, in many applications, what humans require is the ability to perform various tasks directly through a few demonstrations of corresponding tasks, where the agent would meet many unexpected changes when deployed. In this scenario, the agent is expected to not only imitate the demonstration but also adapt to unforeseen environmental changes. This motivates us to propose a new topic called imitator learning (ItorL), which aims to derive an imitator module that can on-the-fly reconstruct the imitation policies based on very limited expert demonstrations for different unseen tasks, without any extra adjustment. In this work, we focus on imitator learning based on only one expert demonstration. To solve ItorL, we propose Demo-Attention Actor-Critic (DAAC), which integrates IL into a reinforcement-learning paradigm that can regularize policies' behaviors in unexpected situations. Besides, for autonomous imitation policy building, we design a demonstration-based attention architecture for imitator policy that can effectively output imitated actions by adaptively tracing the suitable states in demonstrations. We develop a new navigation benchmark and a robot environment for \topic~and show that DAAC~outperforms previous imitation methods \textit{with large margins} both on seen and unseen tasks.
Guiding Language Model Reasoning with Planning Tokens
Authors: Xinyi Wang, Lucas Caccia, Oleksiy Ostapenko, Xingdi Yuan, William Yang Wang, Alessandro Sordoni
Date: 2023-10-09 00:00:00
Tasks: Language Modeling, Language Modelling, Math, model

Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks, such as chain-of-thought (CoT) reasoning. However, most of the existing approaches to enhance this ability rely heavily on data-driven methods, while neglecting the structural aspects of the model's reasoning capacity. To encourage a more structural generation of CoT steps, we propose a hierarchical generation scheme: we let the LM generate a planning token at the start of each reasoning step, intuitively serving as a high-level plan of the current step, and add their embeddings to the model parameters. Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme. We demonstrate our method's effectiveness by applying it to three different LLMs, showing notable accuracy improvements across three math word problem datasets and one multihop QA dataset with respect to standard fine-tuning baselines.
An Attribution Method for Siamese Encoders
Authors: Lucas Möller, Dmitry Nikolaev, Sebastian Padó
Date: 2023-10-09 00:00:00
Tasks: Sentence, STS

Despite the success of Siamese encoder models such as sentence transformers (ST), little is known about the aspects of inputs they pay attention to. A barrier is that their predictions cannot be attributed to individual features, as they compare two inputs rather than processing a single one. This paper derives a local attribution method for Siamese encoders by generalizing the principle of integrated gradients to models with multiple inputs. The solution takes the form of feature-pair attributions, and can be reduced to a token-token matrix for STs. Our method involves the introduction of integrated Jacobians and inherits the advantageous formal properties of integrated gradients: it accounts for the model's full computation graph and is guaranteed to converge to the actual prediction. A pilot study shows that in an ST few token-pairs can often explain large fractions of predictions, and it focuses on nouns and verbs. For accurate predictions, it however needs to attend to the majority of tokens and parts of speech.
Combining recurrent and residual learning for deforestation monitoring using multitemporal SAR images
Authors: Carla Nascimento Neves, Raul Queiroz Feitosa, Mabel X. Ortega Adarme, Gilson Antonio Giraldi
Date: 2023-10-09 00:00:00

With its vast expanse, exceeding that of Western Europe by twice, the Amazon rainforest stands as the largest forest of the Earth, holding immense importance in global climate regulation. Yet, deforestation detection from remote sensing data in this region poses a critical challenge, often hindered by the persistent cloud cover that obscures optical satellite data for much of the year. Addressing this need, this paper proposes three deep-learning models tailored for deforestation monitoring, utilizing SAR (Synthetic Aperture Radar) multitemporal data moved by its independence on atmospheric conditions. Specifically, the study proposes three novel recurrent fully convolutional network architectures-namely, RRCNN-1, RRCNN-2, and RRCNN-3, crafted to enhance the accuracy of deforestation detection. Additionally, this research explores replacing a bitemporal with multitemporal SAR sequences, motivated by the hypothesis that deforestation signs quickly fade in SAR images over time. A comprehensive assessment of the proposed approaches was conducted using a Sentinel-1 multitemporal sequence from a sample site in the Brazilian rainforest. The experimental analysis confirmed that analyzing a sequence of SAR images over an observation period can reveal deforestation spots undetectable in a pair of images. Notably, experimental results underscored the superiority of the multitemporal approach, yielding approximately a five percent enhancement in F1-Score across all tested network architectures. Particularly the RRCNN-1 achieved the highest accuracy and also boasted half the processing time of its closest counterpart.
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Authors: Amr Abourayya, Jens Kleesiek, Kanishka Rao, Erman Ayday, Bharat Rao, Geoff Webb, Michael Kamp
Date: 2023-10-09 00:00:00
Tasks: Federated Learning

In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been proposed to train models locally at each client without sharing their sensitive data, typically by exchanging model parameters, or probabilistic predictions (soft labels) on a public dataset or a combination of both. However, these methods still disclose private information and restrict local models to those that can be trained using gradient-based methods. We propose a federated co-training (FedCT) approach that improves privacy by sharing only definitive (hard) labels on a public unlabeled dataset. Clients use a consensus of these shared labels as pseudo-labels for local training. This federated co-training approach empirically enhances privacy without compromising model quality. In addition, it allows the use of local models that are not suitable for parameter aggregation in traditional federated learning, such as gradient-boosted decision trees, rule ensembles, and random forests. Furthermore, we observe that FedCT performs effectively in federated fine-tuning of large language models, where its pseudo-labeling mechanism is particularly beneficial. Empirical evaluations and theoretical analyses suggest its applicability across a range of federated learning scenarios.
Hierarchical Reinforcement Learning for Temporal Pattern Prediction
Authors: Faith Johnson, Kristin Dana
Date: 2023-10-09 00:00:00
Tasks: Hierarchical Reinforcement Learning, Prediction, reinforcement-learning, Reinforcement Learning

In this work, we explore the use of hierarchical reinforcement learning (HRL) for the task of temporal sequence prediction. Using a combination of deep learning and HRL, we develop a stock agent to predict temporal price sequences from historical stock price data and a vehicle agent to predict steering angles from first person, dash cam images. Our results in both domains indicate that a type of HRL, called feudal reinforcement learning, provides significant improvements to training speed and stability and prediction accuracy over standard RL. A key component to this success is the multi-resolution structure that introduces both temporal and spatial abstraction into the network hierarchy.
Analysis of Rainfall Variability and Water Extent of Selected Hydropower Reservoir Using Google Earth Engine (GEE): A Case Study from Two Tropical Countries, Sri Lanka and Vietnam
Authors: Punsisi Rajakaruna, Surajit Ghosh, Bunyod Holmatov
Date: 2023-10-09 00:00:00
Tasks: Decision Making, Management

This study presents a comprehensive remote sensing analysis of rainfall patterns and selected hydropower reservoir water extent in two tropical monsoon countries, Vietnam and Sri Lanka. The aim is to understand the relationship between remotely sensed rainfall data and the dynamic changes (monthly) in reservoir water extent. The analysis utilizes high-resolution optical imagery and Sentinel-1 Synthetic Aperture Radar (SAR) data to observe and monitor water bodies during different weather conditions, especially during the monsoon season. The average annual rainfall for both countries is determined, and spatiotemporal variations in monthly average rainfall are examined at regional and reservoir basin levels using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset from 1981 to 2022. Water extents are derived for selected reservoirs using Sentinel-1 SAR Ground Range Detected (GRD) images in Vietnam and Sri Lanka from 2017 to 2022. The images are pre-processed and corrected using terrain correction and refined Lee filter. An automated thresholding algorithm, OTSU, distinguishes water and land, taking advantage of both VV and VH polarization data. The connected pixel count threshold is applied to enhance result accuracy. The results indicate a clear relationship between rainfall patterns and reservoir water extent, with increased precipitation during the monsoon season leading to higher water extents in the later months. This study contributes to understanding how rainfall variability impacts reservoir water resources in tropical monsoon regions. The preliminary findings can inform water resource management strategies and support these countries' decision-making processes related to hydropower generation, flood management, and irrigation.
Multi-timestep models for Model-based Reinforcement Learning
Authors: Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
Date: 2023-10-09 00:00:00
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data. A critical challenge of this approach is the compounding of one-step prediction errors as length of the trajectory grows. In this paper we tackle this issue by using a multi-timestep objective to train one-step models. Our objective is a weighted sum of a loss function (e.g., negative log-likelihood) at various future horizons. We explore and test a range of weights profiles. We find that exponentially decaying weights lead to models that significantly improve the long-horizon R2 score. This improvement is particularly noticeable when the models were evaluated on noisy data. Finally, using a soft actor-critic (SAC) agent in pure batch reinforcement learning (RL) and iterated batch RL scenarios, we found that our multi-timestep models outperform or match standard one-step models. This was especially evident in a noisy variant of the considered environment, highlighting the potential of our approach in real-world applications.
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Authors: Feiyi Chen, Zhen Qin, Yingying Zhang, Shuiguang Deng, Yi Xiao, Guansong Pang, Qingsong Wen
Date: 2023-10-09 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Anomaly Detection, Unsupervised Anomaly Detection

Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently. The model trained on old-distribution data is outdated after such changes. Retraining the whole model every time is expensive. Besides, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light and Anti-overfitting Retraining Approach (LARA) for deep variational auto-encoder based time series anomaly detection methods (VAEs). This work aims to make three novel contributions: 1) the retraining process is formulated as a convex problem and can converge at a fast rate as well as prevent overfitting; 2) designing a ruminate block, which leverages the historical data without the need to store them; 3) mathematically proving that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones. Moreover, we have performed many experiments to verify that retraining LARA with even 43 time slots of data from new distribution can result in its competitive F1 Score in comparison with the state-of-the-art anomaly detection models trained with sufficient data. Besides, we verify its light overhead.
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
Authors: Moritz Schauer, Marcel Wienöbst
Date: 2023-10-09 00:00:00
Tasks: Causal Discovery, Graph Sampling

In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the ``Causal Zig-Zag sampler'', that targets a probability distribution over classes of observationally equivalent (Markov equivalent) DAGs. The classes are represented as completed partially directed acyclic graphs (CPDAGs). The non-reversible Markov chain relies on the operators used in Chickering's Greedy Equivalence Search (GES) and is endowed with a momentum variable, which improves mixing significantly as we show empirically. The possible target distributions include posterior distributions based on a prior over DAGs and a Markov equivalent likelihood. We offer an efficient implementation wherein we develop new algorithms for listing, counting, uniformly sampling, and applying possible moves of the GES operators, all of which significantly improve upon the state-of-the-art run-time.
FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports
Authors: Akriti Upreti, Kartavya Kothari, Utkarsh Thukral, Vishal Verma
Date: 2023-10-09 00:00:00

Dream11 takes pride in being a unique platform that enables over 190 million fantasy sports users to demonstrate their skills and connect deeper with their favorite sports. While managing such a scale, one issue we are faced with is duplicate/multiple account creation in the system. This is done by some users with the intent of abusing the platform, typically for bonus offers. The challenge is to detect these multiple accounts before it is too late. We propose a graph-based solution to solve this problem in which we first predict edges/associations between users. Using the edge information we highlight clusters of colluding multiple accounts. In this paper, we talk about our distributed ML system which is deployed to serve and support the inferences from our detection models. The challenge is to do this in real-time in order to take corrective actions. A core part of this setup also involves human-in-the-loop components for validation, feedback, and ground-truth labeling.
Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment
Authors: Daniel Anthes, Sushrut Thorat, Peter König, Tim C. Kietzmann
Date: 2023-10-09 00:00:00
Tasks: Continual Learning

Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks. This phenomenon is commonly referred to as catastrophic forgetting. In this paper, we investigate the representational changes that underlie this performance decrease and identify three distinct processes that together account for the phenomenon. The largest component is a misalignment between hidden representations and readout layers. Misalignment occurs due to learning on additional tasks and causes internal representations to shift. Representational geometry is partially conserved under this misalignment and only a small part of the information is irrecoverably lost. All types of representational changes scale with the dimensionality of hidden representations. These insights have implications for deep learning applications that need to be continuously updated, but may also aid aligning ANN models to the rather robust biological vision.
Binary Classification with Confidence Difference
Date: 2023-10-09 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Binary Classification, Classification, Recommendation Systems

Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. However, collecting pointwise labeling confidence for all training examples can be challenging and time-consuming in real-world scenarios. This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. Instead of pointwise labeling confidence, we are given only unlabeled data pairs with confidence difference that specifies the difference in the probabilities of being positive. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound achieves the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven. Extensive experiments on benchmark data sets and a real-world recommender system data set validate the effectiveness of our proposed approaches in exploiting the supervision information of the confidence difference.
Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction
Authors: Yujie Ding, Shuai Jia, Tianyi Ma, Bingcheng Mao, Xiuze Zhou, Liuliu Li, Dongming Han
Date: 2023-10-09 00:00:00

The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features. We propose a novel framework consisting of two components to surmount these challenges. The first component, the Local-Global (LG) model, introduces three distinct strategies for modeling global information. These approaches are grounded respectively on stock features, the capabilities of LLMs, and a hybrid method combining the two paradigms. The second component, Self-Correlated Reinforcement Learning (SCRL), focuses on aligning the embeddings of financial news generated by LLMs with stock features within the same semantic space. By implementing our framework, we have demonstrated superior performance in Rank Information Coefficient and returns, particularly compared to models relying only on stock features in the China A-share market.
Locality-Aware Generalizable Implicit Neural Representation
Date: 2023-10-09 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Decoder, Image Generation

Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances by modulating its weights or intermediate features using latent codes. However, the expressive power of the state-of-the-art modulation is limited due to its inability to localize and capture fine-grained details of data entities such as specific pixels and rays. To address this issue, we propose a novel framework for generalizable INR that combines a transformer encoder with a locality-aware INR decoder. The transformer encoder predicts a set of latent tokens from a data instance to encode local information into each latent token. The locality-aware INR decoder extracts a modulation vector by selectively aggregating the latent tokens via cross-attention for a coordinate input and then predicts the output by progressively decoding with coarse-to-fine modulation through multiple frequency bandwidths. The selective token aggregation and the multi-band feature modulation enable us to learn locality-aware representation in spatial and spectral aspects, respectively. Our framework significantly outperforms previous generalizable INRs and validates the usefulness of the locality-aware latents for downstream tasks such as image generation.
A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields
Authors: Keyang Ye, Hongzhi Wu, Xin Tong, Kun Zhou
Date: 2023-10-09 00:00:00
Tasks: Lighting Estimation, NeRF, Object

We present the first real-time method for inserting a rigid virtual object into a neural radiance field, which produces realistic lighting and shadowing effects, as well as allows interactive manipulation of the object. By exploiting the rich information about lighting and geometry in a NeRF, our method overcomes several challenges of object insertion in augmented reality. For lighting estimation, we produce accurate, robust and 3D spatially-varying incident lighting that combines the near-field lighting from NeRF and an environment lighting to account for sources not covered by the NeRF. For occlusion, we blend the rendered virtual object with the background scene using an opacity map integrated from the NeRF. For shadows, with a precomputed field of spherical signed distance field, we query the visibility term for any point around the virtual object, and cast soft, detailed shadows onto 3D surfaces. Compared with state-of-the-art techniques, our approach can insert virtual object into scenes with superior fidelity, and has a great potential to be further applied to augmented reality systems.