pwc-archive/papers-with-abstracts huggingface dataset:
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Authors: Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, William Yang Wang
Date: 2023-01-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Few-Shot Learning, GSM8K, In-Context Learning, Language Modeling, Language Modelling, Math, text-classification, Text Classification

In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. However, existing literature has highlighted the sensitivity of this capability to the selection of few-shot demonstrations. Current understandings of the underlying mechanisms by which this capability arises from regular language model pretraining objectives remain disconnected from the real-world LLMs. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models. On this premise, we propose an algorithm to select optimal demonstrations from a set of annotated data with a small LM, and then directly generalize the selected demonstrations to larger LMs. We demonstrate significant improvement over baselines, averaged over eight GPT models on eight real-world text classification datasets. We also demonstrate the real-world usefulness of our algorithm on GSM8K, a math word problem dataset. Our empirical findings support our hypothesis that LLMs implicitly infer a latent variable containing task information.
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
Authors: Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, Aditya Grover
Date: 2023-07-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Benchmarking, Weather Forecasting

Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation and mitigation efforts. In recent years, there has been a surging interest in applying data-driven methods based on machine learning for solving core problems such as weather forecasting and climate downscaling. Despite promising results, much of this progress has been impaired due to the lack of large-scale, open-source efforts for reproducibility, resulting in the use of inconsistent or underspecified datasets, training setups, and evaluations by both domain scientists and artificial intelligence researchers. We introduce ClimateLearn, an open-source PyTorch library that vastly simplifies the training and evaluation of machine learning models for data-driven climate science. ClimateLearn consists of holistic pipelines for dataset processing (e.g., ERA5, CMIP6, PRISM), implementation of state-of-the-art deep learning models (e.g., Transformers, ResNets), and quantitative and qualitative evaluation for standard weather and climate modeling tasks. We supplement these functionalities with extensive documentation, contribution guides, and quickstart tutorials to expand access and promote community growth. We have also performed comprehensive forecasting and downscaling experiments to showcase the capabilities and key features of our library. To our knowledge, ClimateLearn is the first large-scale, open-source effort for bridging research in weather and climate modeling with modern machine learning systems. Our library is available publicly at https://github.com/aditya-grover/climate-learn.
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
Authors: Xingyue Huang, Miguel Romero Orth, İsmail İlkan Ceylan, Pablo Barceló
Date: 2023-02-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Knowledge Graphs, Link Prediction, Representation Learning

Graph neural networks are prominent models for representation learning over graph-structured data. While the capabilities and limitations of these models are well-understood for simple graphs, our understanding remains incomplete in the context of knowledge graphs. Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs pertaining to the prominent task of link prediction. Our analysis entails a unifying perspective on seemingly unrelated models and unlocks a series of other models. The expressive power of various models is characterized via a corresponding relational Weisfeiler-Leman algorithm. This analysis is extended to provide a precise logical characterization of the class of functions captured by a class of graph neural networks. The theoretical findings presented in this paper explain the benefits of some widely employed practical design choices, which are validated empirically.
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Authors: Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob Nicolaus Foerster
Date: 2023-10-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Deep Reinforcement Learning, General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The past decade has seen vast progress in deep reinforcement learning (RL) on the back of algorithms manually designed by human researchers. Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks. Despite impressive initial results from algorithms such as Learned Policy Gradient (LPG), there remains a generalization gap when these algorithms are applied to unseen environments. In this work, we examine how characteristics of the meta-training distribution impact the generalization performance of these algorithms. Motivated by this analysis and building on ideas from Unsupervised Environment Design (UED), we propose a novel approach for automatically generating curricula to maximize the regret of a meta-learned optimizer, in addition to a novel approximation of regret, which we name algorithmic regret (AR). The result is our method, General RL Optimizers Obtained Via Environment Design (GROOVE). In a series of experiments, we show that GROOVE achieves superior generalization to LPG, and evaluate AR against baseline metrics from UED, identifying it as a critical component of environment design in this setting. We believe this approach is a step towards the discovery of truly general RL algorithms, capable of solving a wide range of real-world environments.
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Authors: Haoran You, Huihong Shi, Yipin Guo, Yingyan Celine Lin
Date: 2023-06-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Efficient ViTs, Mixture-of-Experts

Vision Transformers (ViTs) have shown impressive performance and have become a unified backbone for multiple vision tasks. However, both the attention mechanism and multi-layer perceptrons (MLPs) in ViTs are not sufficiently efficient due to dense multiplications, leading to costly training and inference. To this end, we propose to reparameterize pre-trained ViTs with a mixture of multiplication primitives, e.g., bitwise shifts and additions, towards a new type of multiplication-reduced model, dubbed $\textbf{ShiftAddViT}$, which aims to achieve end-to-end inference speedups on GPUs without requiring training from scratch. Specifically, all $\texttt{MatMuls}$ among queries, keys, and values are reparameterized using additive kernels, after mapping queries and keys to binary codes in Hamming space. The remaining MLPs or linear layers are then reparameterized with shift kernels. We utilize TVM to implement and optimize those customized kernels for practical hardware deployment on GPUs. We find that such a reparameterization on attention maintains model accuracy, while inevitably leading to accuracy drops when being applied to MLPs. To marry the best of both worlds, we further propose a new mixture of experts (MoE) framework to reparameterize MLPs by taking multiplication or its primitives as experts, e.g., multiplication and shift, and designing a new latency-aware load-balancing loss. Such a loss helps to train a generic router for assigning a dynamic amount of input tokens to different experts according to their latency. Extensive experiments on various 2D/3D Transformer-based vision tasks consistently validate the effectiveness of our proposed ShiftAddViT, achieving up to $\textbf{5.18$\times$}$ latency reductions on GPUs and $\textbf{42.9}$% energy savings, while maintaining a comparable accuracy as original or efficient ViTs.
Self-Evaluation Guided Beam Search for Reasoning
Authors: Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He, Qizhe Xie
Date: 2023-05-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Arithmetic Reasoning, GSM8K, Language Modeling, Language Modelling, Large Language Model, StrategyQA

Breaking down a problem into intermediate steps has demonstrated impressive performance in Large Language Model (LLM) reasoning. However, the growth of the reasoning chain introduces uncertainty and error accumulation, making it challenging to elicit accurate final results. To tackle this challenge of uncertainty in multi-step reasoning, we introduce a stepwise self-evaluation mechanism to guide and calibrate the reasoning process of LLMs. We propose a decoding algorithm integrating the self-evaluation guidance via stochastic beam search. The self-evaluation guidance serves as a better-calibrated automatic criterion, facilitating an efficient search in the reasoning space and resulting in superior prediction quality. Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness. Our approach surpasses the corresponding Codex-backboned baselines in few-shot accuracy by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K, AQuA, and StrategyQA benchmarks, respectively. Experiment results with Llama-2 on arithmetic reasoning demonstrate the efficiency of our method in outperforming the baseline methods with comparable computational budgets. Further analysis in multi-step reasoning finds our self-evaluation guidance pinpoints logic failures and leads to higher consistency and robustness. Our code is publicly available at https://guideddecoding.github.io/.
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
Authors: Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon
Date: 2023-07-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Reinforcement Learning (RL)

Reinforcement learning (RL) algorithms face two distinct challenges: learning effective representations of past and present observations, and determining how actions influence future returns. Both challenges involve modeling long-term dependencies. The Transformer architecture has been very successful to solve problems that involve long-term dependencies, including in the RL domain. However, the underlying reason for the strong performance of Transformer-based RL methods remains unclear: is it because they learn effective memory, or because they perform effective credit assignment? After introducing formal definitions of memory length and credit assignment length, we design simple configurable tasks to measure these distinct quantities. Our empirical results reveal that Transformers can enhance the memory capability of RL algorithms, scaling up to tasks that require memorizing observations $1500$ steps ago. However, Transformers do not improve long-term credit assignment. In summary, our results provide an explanation for the success of Transformers in RL, while also highlighting an important area for future research and benchmark design. Our code is open-sourced at https://github.com/twni2016/Memory-RL
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Authors: Shengjie Zhu, Abhinav Kumar, Masa Hu, Xiaoming Liu
Date: 2023-06-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 3D Object Detection, Camera Calibration, Depth Estimation, Image Cropping, Image Manipulation, Image Manipulation Detection, object-detection, Object Detection, Pose Estimation

3D sensing for monocular in-the-wild images, e.g., depth estimation and 3D object detection, has become increasingly important. However, the unknown intrinsic parameter hinders their development and deployment. Previous methods for the monocular camera calibration rely on specific 3D objects or strong geometry prior, such as using a checkerboard or imposing a Manhattan World assumption. This work solves the problem from the other perspective by exploiting the monocular 3D prior. Our method is assumption-free and calibrates the complete $4$ Degree-of-Freedom (DoF) intrinsic parameters. First, we demonstrate intrinsic is solved from two well-studied monocular priors, i.e., monocular depthmap, and surface normal map. However, this solution imposes a low-bias and low-variance requirement for depth estimation. Alternatively, we introduce a novel monocular 3D prior, the incidence field, defined as the incidence rays between points in 3D space and pixels in the 2D imaging plane. The incidence field is a pixel-wise parametrization of the intrinsic invariant to image cropping and resizing. With the estimated incidence field, a robust RANSAC algorithm recovers intrinsic. We demonstrate the effectiveness of our method by showing superior performance on synthetic and zero-shot testing datasets. Beyond calibration, we demonstrate downstream applications in image manipulation detection & restoration, uncalibrated two-view pose estimation, and 3D sensing. Codes, models, and data will be held in https://github.com/ShngJZ/WildCamera.
Multiplication-Free Transformer Training via Piecewise Affine Operations
Authors: Atli Kosson, Martin Jaggi
Date: 2023-05-26 00:00:00
Proceeding: NeurIPS 2023 11

Multiplications are responsible for most of the computational cost involved in neural network training and inference. Recent research has thus looked for ways to reduce the cost associated with them. Inspired by Mogami (2020), we replace multiplication with a cheap piecewise affine approximation that is achieved by adding the bit representation of the floating point numbers together as integers. We show that transformers can be trained with the resulting modified matrix multiplications on both vision and language tasks with little to no performance impact, and without changes to the training hyperparameters. We further replace all non-linearities in the networks making them fully and jointly piecewise affine in both inputs and weights. Finally, we show that we can eliminate all multiplications in the entire training process, including operations in the forward pass, backward pass and optimizer update, demonstrating the first successful training of modern neural network architectures in a fully multiplication-free fashion.
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
Authors: Qiaozi Gao, Govind Thattai, Suhaila Shakiah, Xiaofeng Gao, Shreyas Pansare, Vasu Sharma, Gaurav Sukhatme, Hangjie Shi, Bofei Yang, Desheng Zheng, Lucy Hu, Karthika Arumugam, Shui Hu, Matthew Wen, Dinakar Guthy, Cadence Chung, Rohan Khanna, Osman Ipek, Leslie Ball, Kate Bland, Heather Rocker, Yadunandana Rao, Michael Johnston, Reza Ghanadan, Arindam Mandal, Dilek Hakkani Tur, Prem Natarajan
Date: 2023-03-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Instruction Following

We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research. Alexa Arena provides a variety of multi-room layouts and interactable objects, for the creation of human-robot interaction (HRI) missions. With user-friendly graphics and control mechanisms, Alexa Arena supports the development of gamified robotic tasks readily accessible to general human users, thus opening a new venue for high-efficiency HRI data collection and EAI system evaluation. Along with the platform, we introduce a dialog-enabled instruction-following benchmark and provide baseline results for it. We make Alexa Arena publicly available to facilitate research in building generalizable and assistive embodied agents.
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
Authors: Ali Zoljodi, Mohammad Loni, Sadegh Abadijou, Mina Alibeigi & Masoud Daneshtalab
Date: 2022-09-06 00:00:00
Proceeding: International Conference on Artificial Neural Networks 2022 2022 9
Tasks: 3D Lane Detection, Autonomous Driving, Autonomous Vehicles, Lane Detection, Neural Architecture Search, Transfer Learning

Lane detection is one of the most fundamental tasks for autonomous driving. It plays a crucial role in the lateral control and the precise localization of autonomous vehicles. Monocular 3D lane detection methods provide state-of-the-art results for estimating the position of lanes in 3D world coordinates using only the information obtained from the front-view camera. Recent advances in Neural Architecture Search (NAS) facilitate automated optimization of various computer vision tasks. NAS can automatically optimize monocular 3D lane detection methods to enhance the extraction and combination of visual features, consequently reducing computation loads and increasing accuracy. This paper proposes 3DLaneNAS, a multi-objective method that enhances the accuracy of monocular 3D lane detection for both short- and long-distance scenarios while at the same time providing a fair amount of hardware acceleration. 3DLaneNAS utilizes a new multi-objective energy function to optimize the architecture of feature extraction and feature fusion modules simultaneously. Moreover, a transfer learning mechanism is used to improve the convergence of the search process. Experimental results reveal that 3DLaneNAS yields a minimum of 5.2% higher accuracy and 1.33 lower latency over competing methods on the synthetic-3D-lanes dataset. Code is at
Optimal blending of multiple independent prediction models
Authors: Peter Taraba
Date: 2023-02-24 00:00:00
Proceeding: Frontiers in Artificial Intelligence 2023 2
Tasks: Prediction

We derive blending coefficients for the optimal blend of multiple independent prediction models with normal (Gaussian) distribution as well as the variance of the final blend. We also provide lower and upper bound estimation for the final variance and we compare these results with machine learning with counts, where only binary information (feature says yes or no only) is used for every feature and the majority of features agreeing together make the decision.
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Authors: Danqi Liao, Chen Liu, Benjamin W. Christensen, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Date: 2023-12-04 00:00:00

Entropy and mutual information in neural networks provide rich information on the learning process, but they have proven difficult to compute reliably in high dimensions. Indeed, in noisy and high-dimensional data, traditional estimates in ambient dimensions approach a fixed entropy and are prohibitively hard to compute. To address these issues, we leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. Specifically, we define diffusion spectral entropy (DSE) in neural representations of a dataset as well as diffusion spectral mutual information (DSMI) between different variables representing data. First, we show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data that outperform classic Shannon entropy, nonparametric estimation, and mutual information neural estimation (MINE). We then study the evolution of representations in classification networks with supervised learning, self-supervision, or overfitting. We observe that (1) DSE of neural representations increases during training; (2) DSMI with the class label increases during generalizable learning but stays stagnant during overfitting; (3) DSMI with the input signal shows differing trends: on MNIST it increases, while on CIFAR-10 and STL-10 it decreases. Finally, we show that DSE can be used to guide better network initialization and that DSMI can be used to predict downstream classification accuracy across 962 models on ImageNet. The official implementation is available at https://github.com/ChenLiu-1996/DiffusionSpectralEntropy.
Fine-Tuning InstructPix2Pix for Advanced Image Colorization
Authors: Zifeng An, Zijing Xu, Eric Fan, Qi Cao
Date: 2023-12-08 00:00:00
Tasks: Colorization, Image Colorization, Language Modeling, Language Modelling

This paper presents a novel approach to human image colorization by fine-tuning the InstructPix2Pix model, which integrates a language model (GPT-3) with a text-to-image model (Stable Diffusion). Despite the original InstructPix2Pix model's proficiency in editing images based on textual instructions, it exhibits limitations in the focused domain of colorization. To address this, we fine-tuned the model using the IMDB-WIKI dataset, pairing black-and-white images with a diverse set of colorization prompts generated by ChatGPT. This paper contributes by (1) applying fine-tuning techniques to stable diffusion models specifically for colorization tasks, and (2) employing generative models to create varied conditioning prompts. After finetuning, our model outperforms the original InstructPix2Pix model on multiple metrics quantitatively, and we produce more realistically colored images qualitatively. The code for this project is provided on the GitHub Repository https://github.com/AllenAnZifeng/DeepLearning282.
A Review On Table Recognition Based On Deep Learning
Authors: Shi Jiyuan, Shi chunqi
Date: 2023-12-08 00:00:00
Tasks: Data Augmentation, Deep Learning, Table Detection, Table Recognition

Table recognition is using the computer to automatically understand the table, to detect the position of the table from the document or picture, and to correctly extract and identify the internal structure and content of the table. After earlier mainstream approaches based on heuristic rules and machine learning, the development of deep learning techniques has brought a new paradigm to this field. This review mainly discusses the table recognition problem from five aspects. The first part introduces data sets, benchmarks, and commonly used evaluation indicators. This section selects representative data sets, benchmarks, and evaluation indicators that are frequently used by researchers. The second part introduces the table recognition model. This survey introduces the development of the table recognition model, especially the table recognition model based on deep learning. It is generally accepted that table recognition is divided into two stages: table detection and table structure recognition. This section introduces the models that follow this paradigm (TD and TSR). The third part is the End-to-End method, this section introduces some scholars' attempts to use an end-to-end approach to solve the table recognition problem once and for all and the part are Data-centric methods, such as data augmentation, aligning benchmarks, and other methods. The fourth part is the data-centric approach, such as data enhancement, alignment benchmark, and so on. The fifth part summarizes and compares the experimental data in the field of form recognition, and analyzes the mainstream and more advantageous methods. Finally, this paper also discusses the possible development direction and trend of form processing in the future, to provide some ideas for researchers in the field of table recognition. (Resource will be released at https://github.com/Wa1den-jy/Topic-on-Table-Recognition .)