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 checkable	Tyler Cowen	Not checkable	10 Apr 2006	Of course Iran knows this reasoning also Tyler Cowen · Not checkable · 10 Apr 2006
Not checkable	Tyler Cowen	Not checkable	10 Apr 2006	The rumors could be an attempt to scare Iran and thus they are a substitute for attacking. Tyler Cowen · Not checkable · 10 Apr 2006
Not checkable	Tyler Cowen	Not checkable	10 Apr 2006	We make lots of noise, hoping to scare Iran. Tyler Cowen · Not checkable · 10 Apr 2006
Not checkable	Tyler Cowen	Not checkable	10 Apr 2006	Alternatively, you may view current rumors as orchestrated. Tyler Cowen · Not checkable · 10 Apr 2006
Not checkable	Tyler Cowen	Not checkable	10 Apr 2006	it costs little to send Tyler Cowen · Not checkable · 10 Apr 2006
Not checkable	Tyler Cowen	Not checkable	10 Apr 2006	The lock-in effect is a danger. Tyler Cowen · Not checkable · 10 Apr 2006
Not checkable	Tyler Cowen	Not checkable	10 Apr 2006	A true intent to attack might do better as a (relative) surprise. Tyler Cowen · Not checkable · 10 Apr 2006
Unresolvable	Tyler Cowen	Prediction	10 Apr 2006	Iranians might regard a secret US signal as a trick and ignore it. Tyler Cowen · Prediction · 10 Apr 2006	Other global risks
Not checkable	Yann LeCun	Not checkable	2006	In the following, we use the convention that small energy values correspond to highly compatible conﬁgurations of the variables, while… Yann LeCun · Not checkable · 2006
Own records only	Michael Burry	Factual	2006	Burry stated that Scion Capital negotiated very favorable ISDA agreements with all eight of its credit derivatives counterparties. Michael Burry · Factual · 2006	Their own work or organisation
Not researched	Yann LeCun	Factual	2006	Late normalization solves several problems associated with the internal normalization of HMMs and Bayesian nets. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	For decision-making tasks, such as steering a robot, it is merely necessary that the system give the lowest energy to the correct answer. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over all possible variable configurations. Yann LeCun · Factual · 2006	Science and research methods
Not checkable	Yann LeCun	Not checkable	2006	Among the many loss functions proposed in the literature, some are good (with a non-zero margin), and some can be bad. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	In the experiment, learning with the NLL loss was much faster than with the square-square loss. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The loss is minimized by placing the V's apex at Y=X^2, which raises the energies of all other answers because the V has a single minimum. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	However, the convexity of the loss function, while mathematically satisfying, does not seem to be a signiﬁcant practical advantage. Yann LeCun · Not checkable · 2006
Resolved true	Nouriel Roubini	Prediction	2006	The housing bust would lead to a banking and financial crisis as homeowners defaulted on mortgages and banks failed. Nouriel Roubini · Prediction · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	Under the NLL loss, the desired answer may have different, non-zero energies for different X. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	For example, there is no reason to believe that the model in Figure 7 is normalizable over Y . Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Most probabilistic models can be viewed as energy-based models whose energy function satisfies normalizability conditions and whose loss has a particular form. Yann LeCun · Factual · 2006	Science and research methods
Not researched	Yann LeCun	Factual	2006	A linear model with the NLL loss is logistic regression. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The minimum classification error (MCE) loss is non-convex. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	A common approach is to view variational methods and energy-based loss functions as approximations to the probabilistic method. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The GTN system trained with NLL loss in LeCun et al. 1998a assigns a well-defined probability distribution over label sequences, contrary to the claim in Lafferty et al. 2001. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	In speech recognition, segmentation into words and phonemes must occur simultaneously with recognition, while the correct phoneme segmentation is rarely available during training. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Michael Burry	Factual	2006	Recent year-over-year US national home price declines have not been seen since the Great Depression. Michael Burry · Factual · 2006	Economics and markets
Not checkable	Yann LeCun	Not checkable	2006	In a sense, variational methods could be interpreted in the context of EBM as a way to choose a subset of energies to pull up. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The perceptron, hinge and NLL losses can be directly translated to the multiclass case. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	One advantage of the energy-based approach is that it puts very little restrictions on the nature of E. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The perceptron loss has no mechanism for creating an energy gap between the correct answer and incorrect ones. Yann LeCun · Factual · 2006	Science and research methods
Not checkable	Yann LeCun	Not checkable	2006	To some observers, the recent interest in the linear structured model looks like somewhat of a throw-back to the past, and a regression on… Yann LeCun · Not checkable · 2006
Own records only	Michael Burry	Factual	2006	The Scion funds carry credit default swaps on subprime mortgage-backed securities with $1.687 billion in notional value. Michael Burry · Factual · 2006	Their own work or organisation
Not researched	Yann LeCun	Factual	2006	The energy loss does not satisfy condition 3 for a general architecture. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The quality of probability estimates obtained from energies depends on how the energy is estimated from data, not on how energies are converted to probabilities. Yann LeCun · Factual · 2006	Science and research methods
Unresolvable	Michael Burry	Factual	2006	Most mortgages underlying the tranches Scion is short have not yet reached their interest rate reset, the period of highest risk. Michael Burry · Factual · 2006	Economics and markets
Mostly true	Yann LeCun	Factual	2006	The hinge loss with G_W(X)=W^T X and a \|\|W\|\|^2 regularizer gives the linear support vector machine. Yann LeCun · Factual · 2006	AI capabilities
Mostly true	Yann LeCun	Factual	2006	The hinge loss systematically chooses the most offending incorrect answer as the one whose energy is pulled up. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The negative log-likelihood loss pulls up the energies of all incorrect answers at each iteration, including those unlikely to have lower energy than the correct answer. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Michael Burry	Factual	2006	In the example subprime pool, senior tranches A-1A and A-1B make up 79% of the pool and have credit support of 21% of the pool. Michael Burry · Factual · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	EBMs manipulate energies, so no normalization is necessary. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Energy minimization is a convenient way to represent the general process of reasoning and inference. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Any prior knowledge we may have about the task at hand is embedded into the architecture and into the loss function (particularly the… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Because the MEE loss saturates the contribution of any single error, it is more robust to label noise and outliers but is non-convex. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The lack of a margin in the perceptron loss, which may theoretically cause stability problems, was overlooked in LeCun et al. 1998a. Yann LeCun · Factual · 2006	Their own work or organisation
Not researched	Yann LeCun	Factual	2006	The quality of an answer in an energy-based model cannot be measured by its energy alone without considering the energies of all other answers. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	We should emphasize again that this is inconsequential if the model is to be used for prediction, classiﬁcation, or decision-making. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	For E(Y,X)=Y·Gface(X)+(1−Y)T, the minimizing Y is 1 if Gface(X)<T and 0 otherwise. Yann LeCun · Factual · 2006	AI capabilities
Mostly true	Michael Burry	Prediction	2006	Rising delinquencies and defaults will continue to push delinquency-to-credit-support ratios higher on the subprime tranches Scion is short. Michael Burry · Prediction · 2006	Economics and markets
Not checkable	Yann LeCun	Not checkable	2006	The best inference procedure to use often depends on the internal structure of the model. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Inference in the described factor graph can be reduced to finding the shortest path in the graph. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	It is not obvious that the negative log-likelihood loss satisﬁes condition 3. Yann LeCun · Not checkable · 2006
Resolved true	Yann LeCun	Factual	2006	The generalized perceptron loss is always positive because its second term is a lower bound on its first term. Yann LeCun · Factual · 2006	Science and research methods
Mostly true	Yann LeCun	Factual	2006	Support vector Markov models, max-margin Markov networks and conditional random fields are all sequence modeling systems that use linearly parameterized energy factors. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	However, there is no guarantee that all incorrect answers with low energy will be pulled up. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The generalized perceptron loss has a margin of zero. Yann LeCun · Factual · 2006	AI capabilities
Own records only	Michael Burry	Factual	2006	Burry spends nearly all his time on stocks and other long-oriented analysis rather than shorts. Michael Burry · Factual · 2006	Their own work or organisation
Not researched	Yann LeCun	Factual	2006	In the NLL loss gradient, the contrastive term pulls up each answer's energy with a force proportional to that answer's likelihood under the model. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Though this is a trivial problem for a learning machine, it is useful for demonstrating the issues involved in the design of an energy… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	A collapsed energy surface can be avoided by including a contrastive term in the loss function. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	The energy loss is a bad loss function in general, but there are certain forms of energies for which it is a good loss function. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Energy-based learning provides a uniﬁed framework for many probabilistic and non-probabilistic approaches to learning, particularly for… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The perceptron loss and the LVQ2 loss satisfy condition 3 with a margin of zero. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	We conclude that this is a good loss function. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	A GTN can be seen as a multilayer architecture in which the states are trellises, just as a neural net is a multilayer architecture in… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	While the zero margin may limit the robustness of the solution, the perceptron loss seems appropriate as a way to reﬁne a system that was… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	This is best handled by hierarchical, multi-layer architectures that can learn low level features and higher level representations in an… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	A meaningful, uncollapsed result with the perceptron loss is only guaranteed if the model cannot produce a flat energy surface. Yann LeCun · Factual · 2006	Science and research methods
Not checkable	Yann LeCun	Not checkable	2006	Hence, at a high level, discriminative probabilistic models can be seen as a special case of EBMs in which: Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more ﬂexibility in the design of… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	This loss function, though popular for regression and neural network training, cannot be used to train most architectures, because it pushes down on the energy of the desired answer without pulling up on any other energy. Yann LeCun · Factual · 2006	Science and research methods
Not checkable	Yann LeCun	Not checkable	2006	Intuitively, the per-sample loss functional should be designed in such a way that it assigns a low loss to well-behaved energy functions… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Our model may give low energy to wrong answers in these regions, but since the inference algorithm cannot ﬁnd them, they will never appear… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The min-sum procedure only applies to factor graphs that are bipartite trees with no loops. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The partition function over configurations of a loopy graphical model cannot be computed exactly, but loopy belief propagation or other variational methods may approximate it. Yann LeCun · Factual · 2006	AI capabilities
Unresolvable	Michael Burry	Factual	2006	The M9 tranche of the PPSI deal is just under $6 million, less than 1% of the original deal size. Michael Burry · Factual · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	The negative log-likelihood loss for structured models requires summing over all label combinations, which number 2^l for a sequence of l binary labels. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The square-square loss is only suitable for energy functions bounded below by zero, because it pins the correct answer's energy at zero. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	When the best value of the latent variable for a given X and Y is ambiguous, one may consider combining the contributions of the various… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	In the usual scenario, the energy is minimized with respect to the variables to be predicted Y , given the observed variables X. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	In the tutorial's first experiment, the architecture's constraints automatically raise the energy of undesired answers while lowering that of desired answers. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Naturally, the ultimate purpose of learning is to produce a model that will give good answers for new input samples that are not seen… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Choosing the combinations of architecture and loss functions that can learn effectively and efﬁciently is critical to the energy-based… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	It is an interesting advantage of energy-based learning that only the incorrect answers whose energies are pulled up actually matter. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	As long as d ≥ m, the loss function shown in Figure 16(b) satisfies condition 3. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	Each factor can be seen as representing soft constraints between the values of its input variables. Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	More importantly, the very reason for internally normalizing HMMs and Bayesian nets is somewhat contradictory with the idea of training… Yann LeCun · Not checkable · 2006
Not checkable	Yann LeCun	Not checkable	2006	Fortunately, since those answers are not found by the inference algorithm, we do not need to worry about their energies. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The energy loss only works with architectures where pushing down the correct answer's energy automatically raises the energies of other answers. Yann LeCun · Factual · 2006	Science and research methods
Not researched	Michael Burry	Factual	2006	Burry said that as 2005 came to a close, organizers of new mortgage pools included or extended use of extra protections to bolster credit support for the pools' tranches, and that this is why he found many more recent deals much less attractive from a short's perspective than mid-2005 deals. Michael Burry · Factual · 2006	Economics and markets
Not checkable	Yann LeCun	Not checkable	2006	This section discusses the conditions that a loss function must satisfy so that its minimization will result in a model that produces the… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	The generalized margin loss functions discussed satisfy condition 3. Yann LeCun · Factual · 2006	AI capabilities
Not checkable	Yann LeCun	Not checkable	2006	The distinction arises during training: we are given the correct value of Y for a number of training samples, but we are never given the… Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	Energy-based loss functions that pull up the most offending incorrect answer pull up only a single energy per learning iteration. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Michael Burry	Factual	2006	In the PPSI 2005-WLL1 deal, Argent Mortgage and Olympus Mortgage separately originated subprime mortgages and each sold them to Ameriquest Mortgage Company. Michael Burry · Factual · 2006	Economics and markets
Not checkable	Yann LeCun	Not checkable	2006	There seems to be a bias-variance dilemma similar to the one that inﬂuences the generalization performance. Yann LeCun · Not checkable · 2006
Not researched	Yann LeCun	Factual	2006	In the NLL loss, the contrastive term pulls up the energies of all candidate answers. Yann LeCun · Factual · 2006	AI capabilities
Resolved true	Michael Burry	Factual	2006	Park Place Securities Incorporated (PPSI) is the depositor in the PPSI 2005-WLL1 mortgage deal. Michael Burry · Factual · 2006	Economics and markets
Not researched	Yann LeCun	Factual	2006	In the two-class problem, E_C and E_I correspond directly to the energies of the two classes. Yann LeCun · Factual · 2006	AI capabilities
Not researched	Yann LeCun	Factual	2006	The negative log-likelihood loss can only be used if the exponential of the negative energy is integrable over Y. Yann LeCun · Factual · 2006	AI capabilities

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Yann LeCun

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Factual AI capabilities

“The value of Y that minimizes this energy function is 1 (face) if Gface(X) < T and 0 (non-face) otherwise.”

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

For E(Y,X)=Y·Gface(X)+(1−Y)T, the minimizing Y is 1 if Gface(X)<T and 0 otherwise.

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

Cite this claim

Yann LeCun, “For E(Y,X)=Y·Gface(X)+(1−Y)T, the minimizing Y is 1 if Gface(X)<T and 0 otherwise.” (said 2006). Verdict: not researched. Reliability API, claim c_3289f4bbdb7d, method 0.1.0+c876030c. https://epistacklabs.com/claims/c_3289f4bbdb7d

Verdicts are made automatically; how verdicts and scores are made. The same data is on Hugging Face under CC BY 4.0.