Search Results for author:
Found 24 papers, 5 papers with code
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
It is, for example, better at deductive than inductive reasoning.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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RHO ($ρ$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses.
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information.
VScript: Controllable Script Generation with Visual Presentation
In order to offer a customized script tool and inspire professional scriptwriters, we present VScript.
Reproducibility in Optimization: Theoretical Framework and Limits
We initiate a formal study of reproducibility in optimization.
Survey of Hallucination in Natural Language Generation
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
Agnostic Learnability of Halfspaces via Logistic Loss
In this paper, we close this gap by constructing a well-behaved distribution such that the global minimizer of the logistic risk over this distribution only achieves $\Omega(\sqrt{\textrm{OPT}})$ misclassification risk, matching the upper bound in (Frei et al., 2021).
Actor-critic is implicitly biased towards high entropy optimal policies
We show that the simplest actor-critic method -- a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or exploration -- does not merely find an optimal policy, but moreover prefers high entropy optimal policies.
Fast Margin Maximization via Dual Acceleration
We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e. g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of $\widetilde{\mathcal{O}}(1/t^2)$.
Early-stopped neural networks are consistent
This work studies the behavior of shallow ReLU networks trained with the logistic loss via gradient descent on binary classification data where the underlying data distribution is general, and the (optimal) Bayes risk is not necessarily zero.
Generalization bounds via distillation
This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization bounds.
Model Generalization on COVID-19 Fake News Detection
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information.
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
Multi-hop Question Generation with Graph Convolutional Network
Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs.
Gradient descent follows the regularization path for general losses
Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error, but also exhibit an implicit bias.
Directional convergence and alignment in deep learning
In this paper, we show that although the minimizers of cross-entropy and related classification losses are off at infinity, network weights learned by gradient flow converge in direction, with an immediate corollary that network predictions, training errors, and the margin distribution also converge.
Neural tangent kernels, transportation mappings, and universal approximation
This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization, which entails that activations are mostly unchanged, and the network is nearly equivalent to its linearization.
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Recent theoretical work has guaranteed that overparameterized networks trained by gradient descent achieve arbitrarily low training error, and sometimes even low test error.
Approximation power of random neural networks
This paper investigates the approximation power of three types of random neural networks: (a) infinite width networks, with weights following an arbitrary distribution; (b) finite width networks obtained by subsampling the preceding infinite width networks; (c) finite width networks obtained by starting with standard Gaussian initialization, and then adding a vanishingly small correction to the weights.
Characterizing the implicit bias via a primal-dual analysis
On the other hand, with a properly chosen but aggressive step size schedule, we prove $O(1/t)$ rates for both $\ell_2$ margin maximization and implicit bias, whereas prior work (including all first-order methods for the general hard-margin linear SVM problem) proved $\widetilde{O}(1/\sqrt{t})$ margin rates, or $O(1/t)$ margin rates to a suboptimal margin, with an implied (slower) bias rate.
Gradient descent aligns the layers of deep linear networks
This paper establishes risk convergence and asymptotic weight matrix alignment --- a form of implicit regularization --- of gradient flow and gradient descent when applied to deep linear networks on linearly separable data.
Risk and parameter convergence of logistic regression
Gradient descent, when applied to the task of logistic regression, outputs iterates which are biased to follow a unique ray defined by the data.
Social welfare and profit maximization from revealed preferences
Consider the seller's problem of finding optimal prices for her $n$ (divisible) goods when faced with a set of $m$ consumers, given that she can only observe their purchased bundles at posted prices, i. e., revealed preferences.
