GenAI4ELab/papercli-papers huggingface dataset:
Explainable Machine Learning Predictions for the Long-term Performance of Brain-Computer Interfaces
Authors: Morgan E Urdaneta; Nicole C Veit; Renae G Burke; Ian G Malone; Kaleb E Smith; Kevin J Otto
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Brain computer interfaces (BCIs) can decode neural signals to control assistive technologies such as robotic limbs for people with paralysis. Neural recordings from intracortical microelectrodes offer the spatiotemporal resolution (e.g., sortable units) necessary for complex tasks, such as controlling a robotic arm with multiple degrees of freedom. However, the quality of these signals decays over time despite many attempts to prolong their longevity. This decrease in long-term performance limits the implementation of this potentially beneficial technology. Predicting whether a channel will have sortable units across time would mitigate this issue and increase the utility of these devices by reducing uncertainty, yet to-date, no such methods exist. Similarly, it would be useful to understand how variables like time post-implantation, electrochemical characteristics, and electrode design impact the long-term quality of these signals. Here, we obtained longitudinal neural recordings and electrochemical data from freely behaving rats implanted with a custom designed microelectrode array with varying site areas, shank positions, and site depths. This dataset was used to develop an explainable artificial intelligence pipeline that predicts with high accuracy the presence of sortable units on a given channel and elucidates the most important factors leading to these predictions. Our pipeline was able to predict whether a channel will be active with an AUC of 0.79 (95% C.I. 0.73–0.86) on unseen data. The most important features of the model were experimental subject, time post-implantation, and a channel’s previous spike metrics. Electrode site depth was the most important electrode design variable. Our results demonstrate the feasibility of implementing explainable artificial intelligence pipelines for longitudinal BCI studies and support previous reports on how factors like time, inter-animal variability, and cortical depth impact long-term performance of BCIs. These results are an important step forward in improving efficient decoding performance and guiding device development, which stand to advance the field and benefit the lives of human BCI patients.
Visually-augmented pretrained language models for NLP Tasks without Images
Authors: Hangyu Guo; Kun Zhou; Xin Zhao; Qinyu Zhang; Ji-Rong Wen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Although pre-trained language models (PLMs) have shown impressive perfor-
mance by text-only self-supervised training, they are found lack of visual se-
mantics or commonsense, e.g., sizes, shapes and colors of commonplace objects.
Existing solutions often rely on explicit images for visual knowledge augmenta-
tion (requiring time-consuming retrieval or generation), and they also conduct the
augmentation for the whole input text, without considering whether it is actually
needed in specific inputs or tasks. To address these issues, we propose a novel
visually-augmented fine-tuning approach that can be generally applied to various
PLMs or NLP tasks, without using any retrieved or generated images, namely
VAWI. Specifically, we first identify the visually-hungry words (VH-words) from
input text via a token selector, where three different methods have been proposed,
including syntax-, attention- and learning-based strategies. Then, we adopts a
fixed CLIP text encoder to generate the visually-augmented representations of
these VH-words. As it has been pre-trained by visual-language alignment task on
large-scale corpus, it is capable of injecting visual semantics into the aligned text
representations. Finally, the visually-augmented features will be fused and trans-
formed into several pre-designed visual prompts based on VH-words, which can
be inserted into PLMs to enrich the visual semantics in word repersentations. We
conduct extensive experiments on ten NLP tasks, i.e., GLUE benchmark, Com-
monsenseQA, CommonGen and SNLI-VE. Experimental results show that our
approach can consistently improve the performance of BERT, RoBERTa, BART
and T5 at different scales, and outperform several competitive baselines signifi-
cantly. Besides, the generated visual prompts of our framework can also be used
for parameter-efficient tuning, which boosts the performance of T5-3B. We will
make our code, data, and models publicly available.
Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
Authors: Jie Ren; Zhanpeng Zhou; Qirui Chen; Quanshi Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Masking some input variables of a deep neural network (DNN) and computing output changes on the masked input sample represent a typical way to compute attributions of input variables in the sample. People usually mask an input variable using its baseline value. However, there is no theory to examine whether baseline value faithfully represents the absence of an input variable, i.e., removing all signals from the input variable. Fortunately, recent studies (Ren et al., 2023a; Deng et al., 2022a) show that the inference score of a DNN can be strictly disentangled into a set of causal patterns (or concepts) encoded by the DNN. Therefore, we propose to use causal patterns to examine the faithfulness of baseline values. More crucially, it is proven that causal patterns can be explained as the elementary rationale of the Shapley value. Furthermore, we propose a method to learn optimal baseline values, and experimental results have demonstrated its effectiveness.
Bit-Pruning: A Sparse Multiplication-Less Dot-Product
Authors: Yusuke Sekikawa; Shingo Yashima
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Dot-product is a central building block in neural networks.
However, multiplication ($\texttt{mult}$) in dot-product consumes intensive energy and space costs that challenge deployment on resource-constrained edge devices.
In this study, we realize energy-efficient neural networks by exploiting a $\texttt{mult}$-less, sparse dot-product. We first reformulate a dot-product between an integer weight and activation into an equivalent operation comprised of additions followed by bit-shifts ($\texttt{add-shift-add}$).
In this formulation, the number of $\texttt{add}$  operations equals the number of bits of the integer weight in binary format. 
Leveraging this observation, we propose Bit-Pruning, which removes unnecessary bits in each weight value during training to reduce the energy consumption of $\texttt{add-shift-add}$. Bit-Pruning can be seen as soft Weight-Pruning as it prunes bits, not the whole weight element.
In extensive experiments, we demonstrate that sparse $\texttt{mult}$-less networks trained with Bit-Pruning show a better accuracy-energy trade-off than sparse $\texttt{mult}$ networks trained with Weight-Pruning.
How Weakly Supervised Information helps Contrastive Learning
Authors: Jingyi Cui; Weiran Huang; Yisen Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Contrastive learning has shown outstanding performances in both supervised and unsupervised learning. However, little is known about when and how weakly supervised information helps improve contrastive learning, especially from the theoretical perspective. The major challenge is that the existing theory of contrastive learning based on supervised learning frameworks failed to distinguish between supervised and unsupervised contrastive learning. Therefore, we turn to the unsupervised learning frameworks, and based on the posterior probability of labels, we translate the weakly supervised information into a similarity graph under the framework of spectral clustering. In this paper, we investigate two typical weakly supervised learning problems, noisy label learning, and semi-supervised learning, and analyze their influence on contrastive learning within a unified framework. Specifically, we analyze the effect of weakly supervised information on the augmentation graph of unsupervised contrastive learning, and consequently on its corresponding error bound. Numerical experiments are carried out to verify the theoretical findings.
Confidence-Based Feature Imputation for Graphs with Partially Known Features
Authors: Daeho Um; Jiwoong Park; Seulki Park; Jin young Choi
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. However, in cases of high rates of missing features, they were unable to avoid significant performance degradation. To overcome this limitation, we introduce a novel concept of channel-wise confidence in a node feature, which is assigned to each imputed channel feature of a node for reflecting the certainty of the imputation. We then design pseudo-confidence using the channel-wise shortest path distance between a missing-feature node and its nearest known-feature node to replace unavailable true confidence in an actual learning process. Based on the pseudo-confidence, we propose a novel feature imputation scheme that performs channel-wise inter-node diffusion and node-wise inter-channel propagation. The scheme can endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves state-of-the-art accuracy for both semi-supervised node classification and link prediction on various datasets containing a high rate of missing features. Codes are available at https://github.com/daehoum1/pcfi.
Neural Discrete Reinforcement Learning
Authors: Yazhe Niu; Yuan Pu; Chuming Li; Zhenjie Yang; Hongsheng Li; Yu Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Designing effective action spaces for complex environments is a fundamental and challenging problem in reinforcement learning (RL). 
Some recent works have revealed that naive RL algorithms utilizing well-designed handcrafted discrete action spaces can achieve promising results even when dealing with high-dimensional continuous or hybrid decision-making problems. However, elaborately designing such action spaces requires comprehensive domain knowledge.
In this paper, we systemically analyze the advantages of discretization for different action spaces and then propose a unified framework, Neural Discrete Reinforcement Learning (NDRL), to automatically learn how to effectively discretize almost arbitrary action spaces.
Specifically, we propose the Action Discretization Variational AutoEncoder (AD-VAE), an action representation learning method that can learn compact latent action spaces while maintain the essential properties of original environments, such as boundary actions and the relationship between different action dimensions. Moreover, we uncover a key issue that parallel optimization of the AD-VAE and online RL agents is often unstable. To address it, we further design several techniques to adapt RL agents to learned action representations, including latent action remapping and ensemble Q-learning. Quantitative experiments and visualization results demonstrate the efficiency and stability of our proposed framework for complex action spaces in various environments.
Backstepping Temporal Difference Learning
Authors: Han-Dong Lim; Donghwan Lee
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract:  Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications. However, even one of the most elementary RL algorithms, temporal-difference (TD) learning, is known to suffer form divergence issue when the off-policy scheme is used together with linear function approximation. To overcome the divergent behavior, several off-policy TD learning algorithms have been developed until now. In this work, we provide a unified view of such algorithms from a purely control-theoretic perspective. Our method relies on the backstepping technique, which is widely used in nonlinear control theory.
Which Invariance Should We Transfer? A Causal Minimax Learning Approach
Authors: Mingzhou Liu; Xiangyu Zheng; Xinwei Sun; Fang Fang; Yizhou Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A major barrier to deploy current machine learning models lies in their sensitivity to dataset shifts. To resolve this problem, most existing studies attempted to transfer stable information to unseen environments. Among these, graph-based methods causally decomposed the data generating process into stable and mutable mechanisms. By removing the effect of mutable generation, they identified a set of stable predictors. However, a key question regarding robustness remains: which subset of the whole stable information should the model transfer, in order to achieve optimal generalization ability? To answer this question, we provide a comprehensive minimax analysis that fully characterizes conditions for a subset to be optimal. Particularly in general cases, we propose to maximize over mutable mechanisms (i.e., the source of dataset shifts), which is provable to identify the worst-case risk over all environments. This ensures us to select the optimal subset with the minimal worst-case risk. To reduce computational costs, we propose to search over only equivalent classes in terms of worst-case risk, instead of over all subsets. In cases when the searching space is still large, we turn this subset selection problem into a sparse min-max optimization scheme, which enjoys the simplicity and efficiency of implementation. The utility of our methods is demonstrated on the diagnosis of Alzheimer's Disease and gene function prediction.
VQR: Automated Software Vulnerability Repair Through Vulnerability Queries
Authors: Michael Fu; Van Nguyen; Chakkrit Tantithamthavorn; Trung Le; Dinh Phung
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recently, automated vulnerability repair (AVR) approaches have been widely adopted to combat the increasing number of software security issues. In particular, transformer-based models achieve competitive results. While existing models are learned to generate vulnerability repairs, existing AVR models lack a mechanism to provide their models with the precise location of vulnerable code (i.e., models may generate repairs for the non-vulnerable areas). To address this problem, we base our framework on the VIT-based approaches for object detection that learn to locate bounding boxes via the cross-matching between object queries and image patches. We cross-match vulnerability queries and their corresponding vulnerable code areas through the cross-attention mechanism to generate more accurate repairs. To strengthen our cross-matching, we propose to learn a novel vulnerability query mask that greatly focuses on vulnerable code areas and integrate it into the cross-attention. Moreover, we also incorporate the vulnerability query mask into the self-attention to learn embeddings that emphasize the vulnerable areas of a program. Through an extensive evaluation using the real-world 5,417 vulnerabilities, our approach outperforms all of the baseline methods by 3.39%-33.21%. The training code and pre-trained models are available at https://github.com/AVR-VQR/VQR.
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Authors: Yecheng Jason Ma; Shagun Sodhani; Dinesh Jayaraman; Osbert Bastani; Vikash Kumar; Amy Zhang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question. We introduce $\textbf{V}$alue-$\textbf{I}$mplicit $\textbf{P}$re-training (VIP), a self-supervised pre-trained visual representation capable of generating dense and smooth reward functions for unseen robotic tasks. VIP casts representation learning from human videos as an offline goal-conditioned reinforcement learning problem and derives a self-supervised dual goal-conditioned value-function objective that does not depend on actions, enabling pre-training on unlabeled human videos. Theoretically, VIP can be understood as a novel implicit time contrastive objective that generates a temporally smooth embedding, enabling the value function to be implicitly defined via the embedding distance, which can then be used to construct the reward for any goal-image specified downstream task. Trained on large-scale Ego4D human videos and without any fine-tuning on in-domain, task-specific data, VIP can provide dense visual reward for an extensive set of simulated and $\textbf{real-robot}$ tasks, enabling diverse reward-based visual control methods and significantly outperforming all prior pre-trained representations. Notably, VIP can enable simple, few-shot offline RL on a suite of real-world robot tasks with as few as 20 trajectories.
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing
Authors: Tianlong Chen; Chengyue Gong; Daniel Jesus Diaz; Xuxi Chen; Jordan Tyler Wells; qiang liu; Zhangyang Wang; Andrew Ellington; Alex Dimakis; Adam Klivans
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The molecular basis of protein thermal stability is only partially understood and has major significance for drug and vaccine discovery.  The lack of datasets and standardized benchmarks considerably limits learning-based discovery methods. We present \texttt{HotProtein}, a large-scale protein dataset with \textit{growth temperature} annotations of thermostability, containing $182$K amino acid sequences and $3$K folded structures from $230$ different species with a wide temperature range $-20^{\circ}\texttt{C}\sim 120^{\circ}\texttt{C}$. Due to functional domain differences and data scarcity within each species, existing methods fail to generalize well on our dataset. We address this problem through a novel learning framework, consisting of ($1$) Protein structure-aware pre-training (SAP) which leverages 3D information to enhance sequence-based pre-training; ($2$) Factorized sparse tuning (FST) that utilizes low-rank and sparse priors as an implicit regularization, together with feature augmentations. Extensive empirical studies demonstrate that our framework improves thermostability prediction compared to other deep learning models. Finally, we introduce a novel editing algorithm to efficiently generate positive amino acid mutations that improve thermostability. Codes are available in https://github.com/VITA-Group/HotProtein.
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Authors: Chunhui Zhang; Chao Huang; Yijun Tian; Qianlong Wen; Zhongyu Ouyang; Youhuan Li; Yanfang Ye; Chuxu Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Even pruned by the state-of-the-art network compression methods, recent research shows that deep learning model training still suffers from the demand of massive data usage. In particular, Graph Neural Networks (GNNs) trained upon non-Euclidean graph data often encounter relatively higher time costs, due to its irregular and nasty density properties, compared with data in the regular Euclidean space (e.g., image or text). Another natural property accompanied with graphs is class-imbalance which cannot be alleviated even with massive data, therefore hinders GNNs’ ability in generalization. To fully tackle these unpleasant properties, (i) theoretically, we introduce a hypothesis about to what extent a subset of the training data can approximate the full dataset’s learning effectiveness. The effectiveness is further guaranteed and proved by the gradients’ distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative in providing gradients to update model parameters. Moreover, the informative subset evolves dynamically during the training process, for samples that are informative in the current training epoch may not be so in the next one. We refer this observation as dynamic data sparsity. We also notice that sparse subnets pruned from a well-trained GNN sometimes forget the information provided by the informative subset, reflected in their poor performance upon the subset. Based on these findings, we develop a unified data-model dynamic sparsity framework named Graph Decantation (GraphDec) to address challenges brought by training upon a massive class-imbalanced graph dataset. The key idea of GraphDec is to identify the informative subset dynamically during the training process by adopting sparse graph contrastive learning. Extensive experiments on multiple benchmark datasets demonstrate that GraphDec outperforms state-of-the-art baselines for the class-imbalanced graph classification and class-imbalanced node classification tasks, with respect to classification accuracy and data usage efficiency.
ASIF: coupled data turns unimodal models to multimodal without training
Authors: Antonio Norelli; Marco Fumero; Valentino Maiorca; Luca Moschella; Emanuele Rodolà; Francesco Locatello
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Aligning the visual and language spaces requires to train deep neural networks from scratch on giant multimodal datasets; CLIP trains both an image and a text encoder, while LiT manages to train just the latter by taking advantage of a pretrained vision network. In this paper, we show that sparse relative representations are sufficient to align text and images without training any network. Our method relies on readily available single-domain encoders (trained with or without supervision) and a modest (in comparison) number of image-text pairs. ASIF redefines what constitutes a multimodal model by explicitly disentangling memory from processing: here the model is defined by the embedded pairs of all the entries in the multimodal dataset, in addition to the parameters of the two encoders. Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models. Overall, our method represents a simple yet surprisingly strong baseline for foundation multi-modal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.
Compressing multidimensional weather and climate data into neural networks
Authors: Langwen Huang; Torsten Hoefler
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are taken as a compact representation of the original grid-based data. While compression ratios range from 300x to more than 3,000x, our method outperforms the state-of-the-art compressor SZ3 in terms of weighted RMSE, MAE. It can faithfully preserve important large scale atmosphere structures and does not introduce significant artifacts.
When using the resulting neural network as a 790x compressed dataloader to train the WeatherBench forecasting model, its RMSE increases by less than 2%. The three orders of magnitude compression democratizes access to high-resolution climate data and enables numerous new research directions.