Claims · Reliability API

28,548 claims, sorted by newest said. Choose a claim to read it in full.
Verdict	Person	Kind	Said	Claim	Topic
Not researched	Yann LeCun	Factual	2006	The preceding discussion of EBM inference and learning did not require manipulating normalized probability distributions. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Citation	2006	Huang and LeCun (2006) and Collobert et al. (2006) argued that convex loss functions do not guarantee good performance and that non-convex losses may be easier to optimize in practice. Yann LeCun · Citation · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	GTNs are designed for situations where the sequential structure is so complicated that the corresponding dynamical factor graph cannot be… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	They are merely designed for training models to answer a different kind of question than probabilistic models. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each conﬁguration of the variables. Yann LeCun · Not checkable · 2006
Mixed	Michael Burry	Factual	2006	Because home prices rose steadily for a long time, troubled homeowners were able to refinance, take cash out and often reduce monthly payments at the same time, which reduced foreclosure rates. Michael Burry · Factual · 2006	Economics and markets
Not checkable	Yann LeCun	Not checkable	2006	Running MCMC for just a few steps limits computational expense. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The hinge-type loss's second term prevents energy collapse by pushing up points whose energy threatens to go below that of the desired answer. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	For the negative log-likelihood loss, the gradient with respect to E_C is positive and with respect to E_I is negative for any energy values. Yann LeCun · Factual · 2006	AI capabilities
Own records only	Michael Burry	Prediction	2006	Burry expected that, in the worst case, his fund's mortgage short would fully amortize to nil value over the next three years, at an average annual cost of carry of roughly six percent of current assets under management. Michael Burry · Prediction · 2006	Their own work or organisation
Not researched	Yann LeCun	Factual	2006	The probability of training hitting a collapsed solution with the perceptron loss is quite small. Yann LeCun · Factual · 2006	AI capabilities
Resolved true	Yann LeCun	Factual	2006	Inference algorithms in energy-based learning often give only approximate answers or do not guarantee finding the global energy minimum. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	Since the RBF centers U1 and U2 are fixed and distinct, there is a strictly positive lower bound on d1 + d2 for all G_W. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	For the squared-error regression energy, the minimum energy is always zero. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	With this simple architecture, the NLL or perceptron loss would give exactly the same result as the energy loss because their contrastive term is constant. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Since the contrastive sample is always near the desired answer, one can hope that the desired answer will become a local minimum of the… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Local minima are rarely a problem in practice for gradient-based learning because of the high dimensionality of the space. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	One interesting suggestion is to pull up on answers that are always near the correct answer, so as to make the correct answer a local… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Knowing these values would make the recognition task much easier. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	it is important to keep in mind that much of the discussion applies equally well to more complex architectures, as we will see later. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Stochastic gradient methods can be applied to any loss function, including nonconvex ones. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	The only challenge is to compute the gradient of the loss with respect to the neural net outputs by backpropagating gradients through the… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	In many practical situations computing the partition function is intractable or impossible, such as when Y has high cardinality or the integral has no analytical solution. Yann LeCun · Factual · 2006	Science and research methods
Not researched	Yann LeCun	Factual	2006	Hinge, log, LVQ2, MCE, square-square and square-exponential losses can all be described as margin losses. Yann LeCun · Factual · 2006	Science and research methods
Not researched	Michael Burry	Factual	2006	Home prices did not appreciate during the year before this 2006 letter. Michael Burry · Factual · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	With the energy loss, nothing prevents G1 and G2 from ignoring their inputs and producing identical outputs, causing a collapsed flat energy surface. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The set of collapsed solutions is a small piece of the parameter space. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	On average, each answer will be pulled up by the appropriate amount according to the partition function. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	the energy-based approach is bound to be more efﬁcient. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	A distinction should be made between the energy function, which is minimized by the inference process, and the loss functional (introduced… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Second, computing the contrastive term in the negative log-likelihood loss function (or its gradient with respect to W ) may be very… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	A carefully tuned stochastic gradient descent is often considerably faster in practice than sophisticated second-order methods because it exploits redundancy between samples. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	The simplest method is stochastic gradient descent. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Citation	2006	Hadsell et al. (2006) used Siamese architectures with the square-square loss for unsupervised learning of manifolds. Yann LeCun · Citation · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The loss function for the highly non-linear GTN-type parameterization is not convex in W. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	With some architectures, the energy loss can lead to a collapsed solution in which the energy is constant and equal to zero. Yann LeCun · Factual · 2006	Science and research methods
Not checkable	Yann LeCun	Not checkable	2006	Contrastive divergence learning can be seen as an approximation of NLL learning with two shortcuts. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	It would seem natural to train GTNs with one of the generalized margin losses. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	the only consistent way involves turning the collection of energies for all possible outputs into a normalized probability distribution. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Discriminative training often uses iterative gradient-based methods to optimize the loss. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	For many interesting models, computing the integral over Y in the NLL loss is intractable. Yann LeCun · Factual · 2006	AI capabilities
Own records only	Michael Burry	Factual	2006	The Funds held quite a bit of cash in 2006, something that last occurred in the 2001-2002 era. Michael Burry · Factual · 2006	Their own work or organisation
Not checkable	Yann LeCun	Not checkable	2006	Hence probabilistic modeling comes with a high price, and should be avoided when the application does not require it. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Latent variables are very useful in situations where a hidden characteristic of the process being modeled can be inferred from… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	However there are numerous applications where it is convenient to use energy functions that depend on a set of hidden variables Z whose… Yann LeCun · Not checkable · 2006
Unresolvable	Yann LeCun	Factual	2006	The LVQ2 loss is a continuous approximation of the number of classification errors. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	In the third and fourth experiments, using loss functions with explicit contrastive terms, the machine performed as expected and did not collapse. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	Unlike generalized margin losses, the LVQ2 loss is non-convex in the correct and most offending incorrect energies. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	As of 2006, GTNs had never been trained with a generalized margin loss, to the authors' knowledge. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	Probabilistic learning is a special case of energy-based learning in which the loss function is the negative log-likelihood, also called the maximum mutual information criterion. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Locating the best-scoring location in the image consists in minimizing the energy with respect to Y and Z. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	For the squared-error regression energy, the Y minimizing E equals G_W(X). Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	Excellent results were obtained for isolated word recognition with the LVQ2 loss despite its zero margin. Yann LeCun · Factual · 2006	AI capabilities
Mixed	Nouriel Roubini	Prediction	2006	Fannie Mae and Freddie Mac would go bankrupt and the credit crunch would lead to a US recession by some time in 2007. Nouriel Roubini · Prediction · 2006	Economics and markets
Not researched	Michael Burry	Factual	2006	Credit default swap protection contracts on these tranches are pay-as-you-go. Michael Burry · Factual · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	As of 2006, no theory exists for energy-based learning with approximate inference. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	First, the normalization requirement limits the choice of energy functions we can use. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Unfortunately, not all combinations of architectures and loss functions are allowed. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The NLL loss is invariant to global shifts of energy values and depends only on energy differences among the Ys for a given X. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Non-linear factors allow the modeling of highly complex dependencies between inputs and labels Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The dependency between X and Y sometimes cannot be expressed as a function mapping X to Y, e.g. the constraint X^2+Y^2=1. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Probabilistic models have two major disadvantages. Yann LeCun · Not checkable · 2006
Unresolvable	Michael Burry	Prediction	2006	Burry expected the mortgage pools shorted by the Scion funds to see maximum stress during the latter half of 2007. Michael Burry · Prediction · 2006	Economics and markets
Own records only	Michael Burry	Factual	2006	The Scion Funds have roughly a 12% allocation in Asia, lower than in the past. Michael Burry · Factual · 2006	Their own work or organisation
Not checkable	Yann LeCun	Not checkable	2006	In the energybased framework, there is no need to make excuses for breaking the rules. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	This loss function can be optimized with a variety of techniques. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	With the NLL loss the minimum is deeper than with the square-square loss because incorrect answers' energies are pushed up to infinity, with decreasing force. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	Inference by marginalization over latent variables reduces to inference by minimization when β→∞ (zero temperature). Yann LeCun · Factual · 2006	AI capabilities
Resolved true	Yann LeCun	Factual	2006	Each NLL iteration involves considerably more work because pushing up every incorrect answer is expensive when the second term's derivative has no analytical form. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	In 2006, the authors said that, to their knowledge, no systematic experimental study had been published on whether second-order methods bring a significant speed improvement over well-tuned stochastic gradient methods for training these models. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Energy-based learning can be seen as an alternative to probabilistic estimation for prediction, classiﬁcation, or decision-making tasks. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Because energies are uncalibrated, there is no a priori guarantee that separately trained energy-based models have commensurate energy scales. Yann LeCun · Factual · 2006	Science and research methods
Not checkable	Yann LeCun	Not checkable	2006	In this way, the regression on X1 and X2 is implicitly learned through the constraint Y rather than explicitly learned through supervision. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Many existing learning models can be be expressed simply in the framework of energy-based learning. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	What we propose here is to view the probabilistic approach as a special case of a much larger family of energy-based methods. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	One of the alleged advantages of CRFs is that their loss function is convex with respect to the parameters W. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The main problem with the perceptron loss is the absence of a margin. Yann LeCun · Factual · 2006	AI capabilities
Own records only	Michael Burry	Factual	2006	Burry never expected Scion's mortgage short to pay off within one year. Michael Burry · Factual · 2006	Their own work or organisation
Unresolvable	Michael Burry	Prediction	2006	Most of the subprime mortgage pools Scion shorted will likely see maximum foreclosures a little over two years into the life of the pool. Michael Burry · Prediction · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	Because of its zero margin, the generalized perceptron loss could lead to a collapsed energy surface. Yann LeCun · Factual · 2006	AI capabilities
Own records only	Michael Burry	Factual	2006	The Scion Funds hold roughly $2 million in put options focused on the Russell 2000 Index. Michael Burry · Factual · 2006	Their own work or organisation
Not researched	Yann LeCun	Factual	2006	The log loss can be seen as a soft version of the hinge loss with an infinite margin. Yann LeCun · Factual · 2006	Science and research methods
Unresolvable	Michael Burry	Factual	2006	Loss severities on foreclosed subprime mortgages rose during 2006 from 10-15% at the start of the year to 30-45% recently. Michael Burry · Factual · 2006	Economics and markets
Mostly true	Michael Burry	Factual	2006	Burry said the positions "we" are short are now effectively undercollateralized by performing mortgages, pointing to ratios of delinquent, bankrupt, foreclosed and REO loans to total credit support, several of which were above one. Michael Burry · Factual · 2006	Economics and markets
Not researched	Michael Burry	Factual	2006	The secondary market for mortgages is vast and deep thanks to the invention of mortgage-backed securitizations in the 1970s. Michael Burry · Factual · 2006	Economics and markets
Not checkable	Yann LeCun	Not checkable	2006	Properly designing the architecture and the loss function is critical. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Such hidden variables are called latent variables, by analogy with a similar concept in probabilistic modeling. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Learning and inference with EBMs involve minimizing the energy over the set of answers Y and latent variables Z. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	When the energy is linear in W, the hinge loss with L2 regularizer takes the simple form of Eq. 81. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The energy and loss functions must satisfy the stated sufficient conditions to be guaranteed to work in an energy-based setting. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The NLL loss reduces to the generalized perceptron loss as β→∞, and to the log loss when Y has two elements. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The square-exponential loss has an infinite margin and pushes incorrect answers' energies to infinity with exponentially decreasing force. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	Siamese architectures are used to learn similarity metrics with labeled examples. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Graph transformer networks are hierarchical sequence modeling systems in which the objects that are manipulated are trellises containing… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	For other models, one cannot guarantee anything. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Maximizing the conditional likelihood of training samples is equivalent to minimizing the negative log-likelihood loss. Yann LeCun · Factual · 2006	AI capabilities
Unresolvable	Yann LeCun	Factual	2006	There exists at least one point in HP1 at which the loss is less than at all points in HP2. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	With a linear parameterization, training with the energy loss reduces to a convex least-squares problem. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Energy-based methods are equally well justiﬁed as probabilistic methods. Yann LeCun · Not checkable · 2006
Mostly true	Michael Burry	Factual	2006	In a securitization, each trust is divided into tranches, each defined and rated by the degree of subordination protecting its principal from loss. Michael Burry · Factual · 2006	Economics and markets

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Yann LeCun

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Factual AI capabilities

“Unfortunately, there are many interesting models for which computing the integral over Y is intractable.”

LeCun et al., tutorial authors 2006 A Tutorial on Energy-Based Learning (yann.lecun.com)

For many interesting models, computing the integral over Y in the NLL loss is intractable.

Not researched Checkable, but outside the sample that was researched.

Cite this claim

Yann LeCun, “For many interesting models, computing the integral over Y in the NLL loss is intractable.” (said 2006). Verdict: not researched. Reliability API, claim c_a8a9be06d933, method 0.1.0+c876030c. https://epistacklabs.com/claims/c_a8a9be06d933

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